
""" Importing necessary libraries """
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
import scipy.stats as st
import scipy
import sklearn

from matplotlib.cbook import boxplot_stats as bps


""" Cleaning the ../data/img/ folder """
import os
import glob

files = glob.glob('../data/img/*')
for f in files:
    if os.path.isfile(f) and f.endswith('.png'):
        os.remove(f)

files = glob.glob('../data/img/box-plot/*')
for f in files:
    if os.path.isfile(f) and f.endswith('.png'):
        os.remove(f)


""" Reading the dataset """
disp_df = pd.read_csv("../data/disp_st2ns1.txt.bz2",
                      compression="bz2", index_col=0)
comp_df = pd.read_csv("../data/comp_st2ns1.txt.bz2",
                      compression="bz2", index_col=0)


""" Train Test Split (time series) """

# * Make a copy of the dataframe (as Padas dataframe is mutable, therefore uses a reference)
disp_df_copy = disp_df.copy()

# print(disp_df)
# print(disp_df_copy)

# Now we make the train_x, train_y, test_x, test_y splits taking into account the time series
# Note: the time series is ordered by date, therefore we need to split the data in a way that the train data is before the test data
# Note: the 10 first years are used for training and the last two years for testing
# Note: this is done because if not, we will be predicting the past from the future, which leads to errors and overfitting (data leakage) in the model

# * Calculate the number of rows for training and testing
num_rows = disp_df_copy.shape[0]
num_train_rows = int(num_rows * 10/12)  # 10 first years for training, 2 last years for testing

# * Split the data into train and test dataframes (using iloc instead of train_test_split as it picks random rows)
train_df = disp_df_copy.iloc[:num_train_rows, :]  # train contains the first 10 years of rows
test_df = disp_df_copy.iloc[num_train_rows:, :] # test contains the last 2 years of rows

# Print the number of rows for each dataframe
print(f"Number of rows for training (EDA): {train_df.shape[0]}")
print(f"Number of rows for testing: {test_df.shape[0]}")


# ! We maintain the original dataframe for later use (as we will divide it into train and test dataframes below)
# ! For the EDA, we will use the train_df dataframe (with the outpout variable).

Number of rows for training (EDA): 3650
Number of rows for testing: 730


# Display all the columns of the dataframe
pd.set_option('display.max_columns', None)

train_df.describe()


train_df.shape

(3650, 76)


train_df.head()


train_df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 3650 entries, V1 to V3650
Data columns (total 76 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   apcp_sf1_1  3650 non-null   float64
 1   apcp_sf2_1  3650 non-null   float64
 2   apcp_sf3_1  3650 non-null   float64
 3   apcp_sf4_1  3650 non-null   float64
 4   apcp_sf5_1  3650 non-null   float64
 5   dlwrf_s1_1  3650 non-null   float64
 6   dlwrf_s2_1  3650 non-null   float64
 7   dlwrf_s3_1  3650 non-null   float64
 8   dlwrf_s4_1  3650 non-null   float64
 9   dlwrf_s5_1  3650 non-null   float64
 10  dswrf_s1_1  3650 non-null   float64
 11  dswrf_s2_1  3650 non-null   float64
 12  dswrf_s3_1  3650 non-null   float64
 13  dswrf_s4_1  3650 non-null   float64
 14  dswrf_s5_1  3650 non-null   float64
 15  pres_ms1_1  3650 non-null   float64
 16  pres_ms2_1  3650 non-null   float64
 17  pres_ms3_1  3650 non-null   float64
 18  pres_ms4_1  3650 non-null   float64
 19  pres_ms5_1  3650 non-null   float64
 20  pwat_ea1_1  3650 non-null   float64
 21  pwat_ea2_1  3650 non-null   float64
 22  pwat_ea3_1  3650 non-null   float64
 23  pwat_ea4_1  3650 non-null   float64
 24  pwat_ea5_1  3650 non-null   float64
 25  spfh_2m1_1  3650 non-null   float64
 26  spfh_2m2_1  3650 non-null   float64
 27  spfh_2m3_1  3650 non-null   float64
 28  spfh_2m4_1  3650 non-null   float64
 29  spfh_2m5_1  3650 non-null   float64
 30  tcdc_ea1_1  3650 non-null   float64
 31  tcdc_ea2_1  3650 non-null   float64
 32  tcdc_ea3_1  3650 non-null   float64
 33  tcdc_ea4_1  3650 non-null   float64
 34  tcdc_ea5_1  3650 non-null   float64
 35  tcolc_e1_1  3650 non-null   float64
 36  tcolc_e2_1  3650 non-null   float64
 37  tcolc_e3_1  3650 non-null   float64
 38  tcolc_e4_1  3650 non-null   float64
 39  tcolc_e5_1  3650 non-null   float64
 40  tmax_2m1_1  3650 non-null   float64
 41  tmax_2m2_1  3650 non-null   float64
 42  tmax_2m3_1  3650 non-null   float64
 43  tmax_2m4_1  3650 non-null   float64
 44  tmax_2m5_1  3650 non-null   float64
 45  tmin_2m1_1  3650 non-null   float64
 46  tmin_2m2_1  3650 non-null   float64
 47  tmin_2m3_1  3650 non-null   float64
 48  tmin_2m4_1  3650 non-null   float64
 49  tmin_2m5_1  3650 non-null   float64
 50  tmp_2m_1_1  3650 non-null   float64
 51  tmp_2m_2_1  3650 non-null   float64
 52  tmp_2m_3_1  3650 non-null   float64
 53  tmp_2m_4_1  3650 non-null   float64
 54  tmp_2m_5_1  3650 non-null   float64
 55  tmp_sfc1_1  3650 non-null   float64
 56  tmp_sfc2_1  3650 non-null   float64
 57  tmp_sfc3_1  3650 non-null   float64
 58  tmp_sfc4_1  3650 non-null   float64
 59  tmp_sfc5_1  3650 non-null   float64
 60  ulwrf_s1_1  3650 non-null   float64
 61  ulwrf_s2_1  3650 non-null   float64
 62  ulwrf_s3_1  3650 non-null   float64
 63  ulwrf_s4_1  3650 non-null   float64
 64  ulwrf_s5_1  3650 non-null   float64
 65  ulwrf_t1_1  3650 non-null   float64
 66  ulwrf_t2_1  3650 non-null   float64
 67  ulwrf_t3_1  3650 non-null   float64
 68  ulwrf_t4_1  3650 non-null   float64
 69  ulwrf_t5_1  3650 non-null   float64
 70  uswrf_s1_1  3650 non-null   float64
 71  uswrf_s2_1  3650 non-null   float64
 72  uswrf_s3_1  3650 non-null   float64
 73  uswrf_s4_1  3650 non-null   float64
 74  uswrf_s5_1  3650 non-null   float64
 75  salida      3650 non-null   int64  
dtypes: float64(75), int64(1)
memory usage: 2.1+ MB


train_df.isna().sum()

apcp_sf1_1    0
apcp_sf2_1    0
apcp_sf3_1    0
apcp_sf4_1    0
apcp_sf5_1    0
             ..
uswrf_s2_1    0
uswrf_s3_1    0
uswrf_s4_1    0
uswrf_s5_1    0
salida        0
Length: 76, dtype: int64


# In the plot, we can see that there are a lot of 0 values in the dataset
train_df.plot(legend=False, figsize=(15, 5))

<Axes: >


result = train_df.eq(0.0).sum()/len(train_df)*100

# Select those columns with more than 30% of zeros
result = result[result > 30.0]
result = result.sort_values(ascending=False)
result

dswrf_s1_1    91.808219
uswrf_s1_1    90.767123
apcp_sf4_1    63.041096
apcp_sf5_1    61.041096
apcp_sf1_1    60.821918
apcp_sf2_1    59.890411
apcp_sf3_1    56.739726
tcdc_ea3_1    37.917808
tcdc_ea1_1    37.808219
tcdc_ea2_1    37.424658
tcdc_ea5_1    36.301370
tcdc_ea4_1    35.726027
dtype: float64


disp_df_nan = train_df.replace(0.0, np.nan)


""" Plotting missing values """
# Sustitute 0.0 values with NaN and plot the name of the columns with missing values
# ? msno.bar is a simple visualization of nullity by column
msno.bar(disp_df_nan, labels=True, fontsize=7, figsize=(15, 7))

# Exporting image as png to ../data/img folder
plt.savefig("../data/img/missing_values_bar.png")


""" Plotting the missing values in a matrix """

# Fix ValueError: keyword grid_b is not recognized



# ? The msno.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
msno.matrix(disp_df_nan)

# Exporting image as png to ../data/img folder
plt.savefig("../data/img/missing_values_matrix.png")


""" Plotting the missing values in a heatmap """
# As in a hetmap not every value is shown, we must delimit the values to the ones with more than 30% of missing values
result = disp_df.eq(0.0).sum()/len(disp_df)*100
result = result[result > 30.0] # Select those columns with more than 30% of zeros
result = result.sort_values(ascending=False)
result = result.index.tolist() # Convert to list
result

# ? The missingno correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another
msno.heatmap(disp_df_nan[result], fontsize=7, figsize=(15, 7))

# Exporting image as png to ../data/img folder
plt.savefig("../data/img/missing_values_heatmap.png")


""" Plotting the dendrogram """

# ? The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap:
msno.dendrogram(disp_df_nan, orientation="top",fontsize=7, figsize=(15, 7))

# Exporting image as png to ../data/img folder
plt.savefig("../data/img/missing_values_dendrogram.png")


list_of_attributes = train_df.columns.values.tolist()
#print(list_of_attributes)


# Boxplot with all attributes in the dataset
# sns.boxplot(data=train_df, orient="h")
# plt.show()


train_df.describe()


train_df['apcp_sf1_1'].value_counts()

0.000000    2220
0.000909      54
0.001818      24
0.003636      19
0.002727      19
            ... 
2.356364       1
0.920000       1
0.048182       1
0.211818       1
1.363636       1
Name: apcp_sf1_1, Length: 1170, dtype: int64


plt.hist(train_df)
plt.show()


""" Plotting the boxplot for each attribute and getting the outliers of each attribute """
total_outliers = []
# * We iterate over the list of attributes
for attribute in list_of_attributes:
    # * sns.regplot(x=train_df[attribute], y=train_df['total'], fit_reg=False)
    sns.boxplot(data=train_df[attribute], x=train_df[attribute], orient="h")
    # * Use the command below to show each plot (small size for visualization sake)
    # sns.set(rc={'figure.figsize':(1,.5)})
    # plt.show()
    # * All the images are saved in the folder ../data/img/box-plot
    plt.savefig(f"../data/img/box-plot/{str(attribute)}.png")

    # We obtain the a list of outliers for each attribute
    list_of_outliers = train_df[attribute][train_df[attribute] > train_df[attribute].quantile(0.75) + 1.5*(train_df[attribute].quantile(0.75) - train_df[attribute].quantile(0.25))].tolist()
    outliers = [f'{attribute} outliers'] + [len(list_of_outliers)] + [list_of_outliers]
    # * In orde to print the total number of outliers for each attribute
    # print(f'{attribute} has {len(list_of_outliers)} outliers')
    # ! Data structure: [attribute, number of outliers, list of outliers]
    # print(outliers)
    total_outliers.append(outliers)

# print the first 2 elements of each element in the list -> [[atb, num],[atb, num],...]
num_atb_outliers = 0
for i in total_outliers:
    if i[1] != 0:
        num_atb_outliers += 1
        print(f"{i[0:2]}...")
        
# Number of outliers != 0 for each attribute
print(f"Total number of atributes with outliers: {num_atb_outliers} / {len(total_outliers)-1}")

['apcp_sf1_1 outliers', 693]...
['apcp_sf2_1 outliers', 674]...
['apcp_sf3_1 outliers', 677]...
['apcp_sf4_1 outliers', 761]...
['apcp_sf5_1 outliers', 709]...
['dswrf_s1_1 outliers', 299]...
['pres_ms1_1 outliers', 56]...
['pres_ms2_1 outliers', 55]...
['pres_ms3_1 outliers', 64]...
['pres_ms4_1 outliers', 68]...
['pres_ms5_1 outliers', 58]...
['tcdc_ea1_1 outliers', 514]...
['tcdc_ea2_1 outliers', 525]...
['tcdc_ea3_1 outliers', 575]...
['tcdc_ea4_1 outliers', 549]...
['tcdc_ea5_1 outliers', 559]...
['tcolc_e1_1 outliers', 513]...
['tcolc_e2_1 outliers', 523]...
['tcolc_e3_1 outliers', 575]...
['tcolc_e4_1 outliers', 555]...
['tcolc_e5_1 outliers', 560]...
['uswrf_s1_1 outliers', 337]...
['uswrf_s3_1 outliers', 3]...
['uswrf_s4_1 outliers', 31]...
['uswrf_s5_1 outliers', 9]...
Total number of atributes with outliers: 25 / 75


""" Skewness """
# ? skewness: measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
train_df.skew().sort_values(ascending=False)

apcp_sf4_1    9.297678
apcp_sf2_1    7.610005
apcp_sf5_1    7.244491
apcp_sf3_1    7.241727
apcp_sf1_1    6.783553
                ...   
ulwrf_t1_1   -0.964701
ulwrf_t3_1   -0.989917
ulwrf_t2_1   -1.001763
ulwrf_t5_1   -1.071147
ulwrf_t4_1   -1.196425
Length: 76, dtype: float64


""" Kurtosis """
# ? kurtosis: measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution.
train_df.kurt().sort_values(ascending=False)

apcp_sf4_1    138.601323
apcp_sf2_1     79.535762
apcp_sf5_1     78.321580
apcp_sf3_1     72.498316
apcp_sf1_1     61.204708
                 ...    
uswrf_s2_1     -1.306893
spfh_2m2_1     -1.320499
spfh_2m5_1     -1.321073
dswrf_s2_1     -1.323864
spfh_2m3_1     -1.329847
Length: 76, dtype: float64


y = train_df['apcp_sf4_1']
plt.figure(1); plt.title('Normal')
sns.distplot(y, kde=True, fit=st.norm)
plt.figure(2); plt.title('Log Normal')
sns.distplot(y, kde=True, fit=st.lognorm)

/tmp/ipykernel_3928/2851594757.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(y, kde=True, fit=st.norm)
/tmp/ipykernel_3928/2851594757.py:5: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(y, kde=True, fit=st.lognorm)

<Axes: title={'center': 'Log Normal'}, xlabel='apcp_sf4_1', ylabel='Density'>


sns.distplot(train_df.skew(),color='blue',axlabel ='Skewness')

/tmp/ipykernel_3928/3675943835.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(train_df.skew(),color='blue',axlabel ='Skewness')

<Axes: xlabel='Skewness', ylabel='Density'>


plt.figure(figsize = (12,8))
sns.distplot(train_df.kurt(),color='r',axlabel ='Kurtosis',norm_hist= False, kde = True,rug = False)
#plt.hist(train.kurt(),orientation = 'vertical',histtype = 'bar',label ='Kurtosis', color ='blue')
plt.show()

/tmp/ipykernel_3928/1886886254.py:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(train_df.kurt(),color='r',axlabel ='Kurtosis',norm_hist= False, kde = True,rug = False)


correlation = train_df.corr()
correlation = abs(correlation)
print(correlation.shape)  # 76 x 76 matrix of correlation values

(76, 76)


correlation_list = []

for column in train_df.columns:
    correlation.loc[:, column] = abs(correlation.iloc[:, train_df.columns.get_loc(column)])
    mask = correlation.loc[:, column] > 0.95
    # print(correlation[column][mask].sort_values(ascending = False))
    
    # Translate the comment below to English: 
    # we add the correlation values to a list of lists, which contains the names of the correlated columns and their correlation index
    
    # The first segment adds the name of the column we are analyzing
    # The second segment adds the names of the columns correlated (except the column we are analyzing) > 0.95
    # The third segment adds the correlation index of the columns correlated (except the column we are analyzing) > 0.95
    # Second and third segment are added to the first segment as a list of lists
    
    # First we need to create a dictionary with the column names and their correlation values (except the column we are analyzing)
    dict = ({key: value for key, value in correlation.loc[column, mask].sort_values(ascending = False).iloc[1:].to_dict().items()})
    # print (dict)
    
    # Then we create a list of lists with the column names and their correlation values from the dictionary created above
    corr_list = [[key] + [value] for key, value in dict.items()]
    # Finally we add the name of the column we are analyzing to the list of lists created above as the first element of the list (str)
    corr_list.insert(0, ["Columna: " + column])
    
    # ! Data structure: [[columna, [columna correlada 1, indice de correlacion], [columna correlada 2, indice de correlacion], ...], ...] 
    print(corr_list)
    
    correlation_list += [corr_list]
print(correlation_list)

[['Columna: apcp_sf1_1']]
[['Columna: apcp_sf2_1']]
[['Columna: apcp_sf3_1']]
[['Columna: apcp_sf4_1']]
[['Columna: apcp_sf5_1']]
[['Columna: dlwrf_s1_1'], ['dlwrf_s2_1', 0.9650067922254768], ['dlwrf_s3_1', 0.9547817730760655]]
[['Columna: dlwrf_s2_1'], ['dlwrf_s3_1', 0.993701215706055], ['dlwrf_s1_1', 0.9650067922254768]]
[['Columna: dlwrf_s3_1'], ['dlwrf_s2_1', 0.993701215706055], ['dlwrf_s4_1', 0.9659874690575408], ['dlwrf_s5_1', 0.9552712673845433], ['dlwrf_s1_1', 0.9547817730760655]]
[['Columna: dlwrf_s4_1'], ['dlwrf_s5_1', 0.9969222914149775], ['dlwrf_s3_1', 0.9659874690575408]]
[['Columna: dlwrf_s5_1'], ['dlwrf_s4_1', 0.9969222914149775], ['dlwrf_s3_1', 0.9552712673845433]]
[['Columna: dswrf_s1_1']]
[['Columna: dswrf_s2_1'], ['uswrf_s2_1', 0.9911709851006711], ['dswrf_s3_1', 0.9503896354343679]]
[['Columna: dswrf_s3_1'], ['uswrf_s2_1', 0.9591814530708258], ['dswrf_s2_1', 0.9503896354343679]]
[['Columna: dswrf_s4_1'], ['dswrf_s5_1', 0.982758557897581]]
[['Columna: dswrf_s5_1'], ['dswrf_s4_1', 0.982758557897581]]
[['Columna: pres_ms1_1'], ['pres_ms2_1', 0.9879236602955379], ['pres_ms3_1', 0.956852960202746]]
[['Columna: pres_ms2_1'], ['pres_ms1_1', 0.9879236602955379], ['pres_ms3_1', 0.9869377705171734], ['pres_ms4_1', 0.9536176398645005]]
[['Columna: pres_ms3_1'], ['pres_ms2_1', 0.9869377705171734], ['pres_ms4_1', 0.9866602703072012], ['pres_ms1_1', 0.956852960202746], ['pres_ms5_1', 0.9538147697170144]]
[['Columna: pres_ms4_1'], ['pres_ms3_1', 0.9866602703072012], ['pres_ms5_1', 0.9851755074525863], ['pres_ms2_1', 0.9536176398645005]]
[['Columna: pres_ms5_1'], ['pres_ms4_1', 0.9851755074525863], ['pres_ms3_1', 0.9538147697170144]]
[['Columna: pwat_ea1_1'], ['pwat_ea2_1', 0.9859484994851248], ['pwat_ea3_1', 0.9577107162594556]]
[['Columna: pwat_ea2_1'], ['pwat_ea3_1', 0.9874259658433963], ['pwat_ea1_1', 0.9859484994851248], ['pwat_ea4_1', 0.9618712300670131]]
[['Columna: pwat_ea3_1'], ['pwat_ea4_1', 0.9880603787665849], ['pwat_ea2_1', 0.9874259658433963], ['pwat_ea5_1', 0.9616424908340101], ['pwat_ea1_1', 0.9577107162594556]]
[['Columna: pwat_ea4_1'], ['pwat_ea3_1', 0.9880603787665849], ['pwat_ea5_1', 0.986763801908917], ['pwat_ea2_1', 0.9618712300670131]]
[['Columna: pwat_ea5_1'], ['pwat_ea4_1', 0.986763801908917], ['pwat_ea3_1', 0.9616424908340101]]
[['Columna: spfh_2m1_1'], ['spfh_2m2_1', 0.9742691195680059]]
[['Columna: spfh_2m2_1'], ['spfh_2m3_1', 0.9846069576918387], ['spfh_2m1_1', 0.9742691195680059], ['spfh_2m4_1', 0.9600698332225309]]
[['Columna: spfh_2m3_1'], ['spfh_2m4_1', 0.9891201306737782], ['spfh_2m2_1', 0.9846069576918387], ['spfh_2m5_1', 0.9771699520274281]]
[['Columna: spfh_2m4_1'], ['spfh_2m5_1', 0.9904262248914517], ['spfh_2m3_1', 0.9891201306737782], ['spfh_2m2_1', 0.9600698332225309]]
[['Columna: spfh_2m5_1'], ['spfh_2m4_1', 0.9904262248914517], ['spfh_2m3_1', 0.9771699520274281]]
[['Columna: tcdc_ea1_1'], ['tcolc_e1_1', 0.9999826963362115]]
[['Columna: tcdc_ea2_1'], ['tcolc_e2_1', 0.9999837132775715]]
[['Columna: tcdc_ea3_1'], ['tcolc_e3_1', 0.9999845616560729]]
[['Columna: tcdc_ea4_1'], ['tcolc_e4_1', 0.999984785893167]]
[['Columna: tcdc_ea5_1'], ['tcolc_e5_1', 0.9999746391911669]]
[['Columna: tcolc_e1_1'], ['tcdc_ea1_1', 0.9999826963362115]]
[['Columna: tcolc_e2_1'], ['tcdc_ea2_1', 0.9999837132775715]]
[['Columna: tcolc_e3_1'], ['tcdc_ea3_1', 0.9999845616560729]]
[['Columna: tcolc_e4_1'], ['tcdc_ea4_1', 0.999984785893167]]
[['Columna: tcolc_e5_1'], ['tcdc_ea5_1', 0.9999746391911669]]
[['Columna: tmax_2m1_1'], ['ulwrf_s1_1', 0.9925465923917536], ['tmin_2m1_1', 0.9864627566914622], ['tmp_2m_1_1', 0.9844648786308661], ['tmp_sfc1_1', 0.9826171735169642], ['tmin_2m2_1', 0.9794577781348043], ['tmin_2m3_1', 0.97879484323375], ['ulwrf_s2_1', 0.9707221719806952], ['tmax_2m2_1', 0.9637997824764319], ['tmp_2m_2_1', 0.9602996922677912], ['ulwrf_s3_1', 0.9578768560091268], ['tmp_sfc2_1', 0.9528200105400535]]
[['Columna: tmax_2m2_1'], ['tmp_2m_2_1', 0.9993112900738094], ['tmp_sfc2_1', 0.9963595768447152], ['ulwrf_s3_1', 0.9943559569788851], ['ulwrf_s2_1', 0.9917542607726626], ['tmax_2m3_1', 0.9863610206089146], ['tmp_2m_3_1', 0.9829472636912582], ['tmin_2m2_1', 0.9819461458841241], ['tmin_2m3_1', 0.9819334574863472], ['tmin_2m4_1', 0.9790285687297058], ['tmp_2m_1_1', 0.9771397511742945], ['tmin_2m1_1', 0.9735093237780129], ['tmin_2m5_1', 0.9713195080332866], ['tmax_2m4_1', 0.9698857690834339], ['tmax_2m5_1', 0.9697541323437304], ['tmp_sfc1_1', 0.9697293778574791], ['tmp_sfc5_1', 0.9692885564165558], ['tmp_2m_5_1', 0.9675902215544175], ['tmp_2m_4_1', 0.9645870257681579], ['tmax_2m1_1', 0.9637997824764319], ['ulwrf_s1_1', 0.9606784450210313], ['ulwrf_s5_1', 0.9582268665523904], ['tmp_sfc3_1', 0.9540154794130064]]
[['Columna: tmax_2m3_1'], ['tmp_2m_3_1', 0.9989979986044517], ['tmin_2m4_1', 0.9973034556239585], ['tmax_2m4_1', 0.9937711137699543], ['tmax_2m5_1', 0.9931925682521422], ['tmp_2m_4_1', 0.9902875444682745], ['ulwrf_s3_1', 0.9882838798756105], ['tmp_2m_2_1', 0.9880399268817291], ['tmp_sfc2_1', 0.9874628996347063], ['tmax_2m2_1', 0.9863610206089146], ['tmin_2m5_1', 0.9849440628695366], ['tmp_sfc3_1', 0.9836268315982332], ['ulwrf_s5_1', 0.9831073628529786], ['tmp_2m_5_1', 0.9800734262132967], ['ulwrf_s4_1', 0.9784038012441758], ['tmp_sfc4_1', 0.977121003001589], ['tmp_sfc5_1', 0.9751782371847001], ['ulwrf_s2_1', 0.966905126635995], ['tmin_2m3_1', 0.9565532246066281], ['tmin_2m2_1', 0.9554925734198499]]
[['Columna: tmax_2m4_1'], ['tmax_2m5_1', 0.999855391919824], ['tmp_2m_4_1', 0.9989670084606509], ['tmin_2m4_1', 0.9965267117336937], ['tmp_2m_3_1', 0.9954824569443925], ['tmax_2m3_1', 0.9937711137699543], ['ulwrf_s5_1', 0.9915793553887863], ['tmp_sfc4_1', 0.9903005164322048], ['tmin_2m5_1', 0.9888950622930037], ['ulwrf_s4_1', 0.987077087459209], ['tmp_2m_5_1', 0.9862698111992504], ['tmp_sfc3_1', 0.9857825429301692], ['tmp_sfc5_1', 0.9780961580272722], ['ulwrf_s3_1', 0.975826585247475], ['tmp_sfc2_1', 0.9734162968894661], ['tmp_2m_2_1', 0.9730783300205592], ['tmax_2m2_1', 0.9698857690834339]]
[['Columna: tmax_2m5_1'], ['tmax_2m4_1', 0.999855391919824], ['tmp_2m_4_1', 0.9988753498975144], ['tmin_2m4_1', 0.9959215026917639], ['tmp_2m_3_1', 0.9948623637802105], ['tmax_2m3_1', 0.9931925682521422], ['ulwrf_s5_1', 0.99151293872598], ['tmp_sfc4_1', 0.9902303273948138], ['tmin_2m5_1', 0.9891405894645761], ['tmp_2m_5_1', 0.9872536923433799], ['ulwrf_s4_1', 0.9863521279974694], ['tmp_sfc3_1', 0.9848432369308402], ['tmp_sfc5_1', 0.9791492889290042], ['ulwrf_s3_1', 0.975536953838263], ['tmp_sfc2_1', 0.9732760733014082], ['tmp_2m_2_1', 0.9729567511523166], ['tmax_2m2_1', 0.9697541323437304]]
[['Columna: tmin_2m1_1'], ['tmp_2m_1_1', 0.9981185076252812], ['tmp_sfc1_1', 0.9963142972792025], ['tmin_2m2_1', 0.9950949108277914], ['tmin_2m3_1', 0.9943408586923532], ['ulwrf_s1_1', 0.9926165091998508], ['tmax_2m1_1', 0.9864627566914622], ['ulwrf_s2_1', 0.9814254257054185], ['tmax_2m2_1', 0.9735093237780129], ['tmp_2m_2_1', 0.9708467193246784], ['ulwrf_s3_1', 0.9654648681213764], ['tmp_sfc2_1', 0.9605896048258724]]
[['Columna: tmin_2m2_1'], ['tmin_2m3_1', 0.9996572623026859], ['tmp_2m_1_1', 0.9981819106379802], ['tmp_sfc1_1', 0.9960749292152973], ['tmin_2m1_1', 0.9950949108277914], ['ulwrf_s2_1', 0.9899721168841563], ['ulwrf_s1_1', 0.9850580289147287], ['tmax_2m2_1', 0.9819461458841241], ['tmp_2m_2_1', 0.9808603480091717], ['tmax_2m1_1', 0.9794577781348043], ['ulwrf_s3_1', 0.9743529570168544], ['tmp_sfc2_1', 0.9709866438853475], ['tmax_2m3_1', 0.9554925734198499], ['tmp_2m_3_1', 0.9516543862813964]]
[['Columna: tmin_2m3_1'], ['tmin_2m2_1', 0.9996572623026859], ['tmp_2m_1_1', 0.9973859703817012], ['tmp_sfc1_1', 0.9951903927420194], ['tmin_2m1_1', 0.9943408586923532], ['ulwrf_s2_1', 0.9897318251363909], ['ulwrf_s1_1', 0.9843176374301973], ['tmax_2m2_1', 0.9819334574863472], ['tmp_2m_2_1', 0.9813049765366397], ['tmax_2m1_1', 0.97879484323375], ['ulwrf_s3_1', 0.9751256747221002], ['tmp_sfc2_1', 0.9715874768684567], ['tmax_2m3_1', 0.9565532246066281], ['tmp_2m_3_1', 0.9537926818714694]]
[['Columna: tmin_2m4_1'], ['tmp_2m_3_1', 0.9989480053798309], ['tmax_2m3_1', 0.9973034556239585], ['tmax_2m4_1', 0.9965267117336937], ['tmax_2m5_1', 0.9959215026917639], ['tmp_2m_4_1', 0.9955546180266533], ['tmin_2m5_1', 0.9884708317878447], ['ulwrf_s5_1', 0.9881522932211056], ['tmp_sfc3_1', 0.985897294709102], ['tmp_sfc4_1', 0.9839686317112281], ['ulwrf_s4_1', 0.9837254094808376], ['tmp_2m_5_1', 0.9836800034039297], ['ulwrf_s3_1', 0.9834305304179148], ['tmp_2m_2_1', 0.9819192741530762], ['tmp_sfc2_1', 0.9818609853360799], ['tmax_2m2_1', 0.9790285687297058], ['tmp_sfc5_1', 0.9773521584222015], ['ulwrf_s2_1', 0.9587402415390521]]
[['Columna: tmin_2m5_1'], ['tmp_2m_5_1', 0.9985248666465218], ['tmp_sfc5_1', 0.9960054258735697], ['tmp_2m_4_1', 0.9895327574093381], ['tmax_2m5_1', 0.9891405894645761], ['tmax_2m4_1', 0.9888950622930037], ['tmin_2m4_1', 0.9884708317878447], ['ulwrf_s5_1', 0.9873222874147335], ['tmp_2m_3_1', 0.9863630955598195], ['tmax_2m3_1', 0.9849440628695366], ['tmp_sfc4_1', 0.9799245981499726], ['ulwrf_s3_1', 0.9771636437810954], ['tmp_sfc2_1', 0.975307217691161], ['tmp_2m_2_1', 0.973997775172227], ['ulwrf_s4_1', 0.9737488476761204], ['tmax_2m2_1', 0.9713195080332866], ['tmp_sfc3_1', 0.9704219757785946], ['ulwrf_s2_1', 0.9584808725553905]]
[['Columna: tmp_2m_1_1'], ['tmin_2m2_1', 0.9981819106379802], ['tmin_2m1_1', 0.9981185076252812], ['tmp_sfc1_1', 0.9979448017549044], ['tmin_2m3_1', 0.9973859703817012], ['ulwrf_s1_1', 0.9897625110599417], ['ulwrf_s2_1', 0.9853437253651708], ['tmax_2m1_1', 0.9844648786308661], ['tmax_2m2_1', 0.9771397511742945], ['tmp_2m_2_1', 0.9744909003873826], ['ulwrf_s3_1', 0.968308266178652], ['tmp_sfc2_1', 0.9638593940360212]]
[['Columna: tmp_2m_2_1'], ['tmax_2m2_1', 0.9993112900738094], ['tmp_sfc2_1', 0.9971658992806404], ['ulwrf_s3_1', 0.994870585784455], ['ulwrf_s2_1', 0.990609269111517], ['tmax_2m3_1', 0.9880399268817291], ['tmp_2m_3_1', 0.9856921834337115], ['tmin_2m4_1', 0.9819192741530762], ['tmin_2m3_1', 0.9813049765366397], ['tmin_2m2_1', 0.9808603480091717], ['tmp_2m_1_1', 0.9744909003873826], ['tmin_2m5_1', 0.973997775172227], ['tmax_2m4_1', 0.9730783300205592], ['tmax_2m5_1', 0.9729567511523166], ['tmp_sfc5_1', 0.9714528776518785], ['tmin_2m1_1', 0.9708467193246784], ['tmp_2m_5_1', 0.9702697100229983], ['tmp_2m_4_1', 0.9679046544965666], ['tmp_sfc1_1', 0.9668200402557023], ['ulwrf_s5_1', 0.960980140611558], ['tmax_2m1_1', 0.9602996922677912], ['ulwrf_s1_1', 0.9573813147342563], ['tmp_sfc3_1', 0.9570765900532514]]
[['Columna: tmp_2m_3_1'], ['tmax_2m3_1', 0.9989979986044517], ['tmin_2m4_1', 0.9989480053798309], ['tmax_2m4_1', 0.9954824569443925], ['tmax_2m5_1', 0.9948623637802105], ['tmp_2m_4_1', 0.9925508354894933], ['ulwrf_s3_1', 0.986475159576972], ['tmin_2m5_1', 0.9863630955598195], ['tmp_2m_2_1', 0.9856921834337115], ['tmp_sfc3_1', 0.9856020769129077], ['tmp_sfc2_1', 0.9853721430018374], ['ulwrf_s5_1', 0.9850287211532148], ['tmax_2m2_1', 0.9829472636912582], ['tmp_2m_5_1', 0.9813875596975576], ['ulwrf_s4_1', 0.9807390701133252], ['tmp_sfc4_1', 0.9796642142509698], ['tmp_sfc5_1', 0.9758811197330793], ['ulwrf_s2_1', 0.9631310421561378], ['tmin_2m3_1', 0.9537926818714694], ['tmin_2m2_1', 0.9516543862813964]]
[['Columna: tmp_2m_4_1'], ['tmax_2m4_1', 0.9989670084606509], ['tmax_2m5_1', 0.9988753498975144], ['tmin_2m4_1', 0.9955546180266533], ['ulwrf_s5_1', 0.9926662202081515], ['tmp_2m_3_1', 0.9925508354894933], ['tmp_sfc4_1', 0.9924835965861123], ['tmax_2m3_1', 0.9902875444682745], ['tmin_2m5_1', 0.9895327574093381], ['ulwrf_s4_1', 0.9875947598774877], ['tmp_2m_5_1', 0.9871404965220815], ['tmp_sfc3_1', 0.9838690005793208], ['tmp_sfc5_1', 0.9781934487046426], ['ulwrf_s3_1', 0.9712226930987012], ['tmp_sfc2_1', 0.9684774894714101], ['tmp_2m_2_1', 0.9679046544965666], ['tmax_2m2_1', 0.9645870257681579]]
[['Columna: tmp_2m_5_1'], ['tmin_2m5_1', 0.9985248666465218], ['tmp_sfc5_1', 0.9980075740584928], ['tmax_2m5_1', 0.9872536923433799], ['tmp_2m_4_1', 0.9871404965220815], ['tmax_2m4_1', 0.9862698111992504], ['ulwrf_s5_1', 0.9852702329370912], ['tmin_2m4_1', 0.9836800034039297], ['tmp_2m_3_1', 0.9813875596975576], ['tmax_2m3_1', 0.9800734262132967], ['tmp_sfc4_1', 0.9781233238955636], ['ulwrf_s3_1', 0.9731828651939985], ['tmp_sfc2_1', 0.971798288488127], ['tmp_2m_2_1', 0.9702697100229983], ['ulwrf_s4_1', 0.9695091232286572], ['tmax_2m2_1', 0.9675902215544175], ['tmp_sfc3_1', 0.9646544198978636], ['ulwrf_s2_1', 0.9560882131646383]]
[['Columna: tmp_sfc1_1'], ['tmp_2m_1_1', 0.9979448017549044], ['tmin_2m1_1', 0.9963142972792025], ['tmin_2m2_1', 0.9960749292152973], ['tmin_2m3_1', 0.9951903927420194], ['ulwrf_s1_1', 0.9919281097281679], ['ulwrf_s2_1', 0.9842676480547997], ['tmax_2m1_1', 0.9826171735169642], ['tmax_2m2_1', 0.9697293778574791], ['tmp_2m_2_1', 0.9668200402557023], ['ulwrf_s3_1', 0.9621315029521358], ['tmp_sfc2_1', 0.957188784436685]]
[['Columna: tmp_sfc2_1'], ['tmp_2m_2_1', 0.9971658992806404], ['ulwrf_s3_1', 0.996855293888584], ['tmax_2m2_1', 0.9963595768447152], ['ulwrf_s2_1', 0.9879322271553737], ['tmax_2m3_1', 0.9874628996347063], ['tmp_2m_3_1', 0.9853721430018374], ['tmin_2m4_1', 0.9818609853360799], ['tmin_2m5_1', 0.975307217691161], ['tmp_sfc5_1', 0.9740593607013978], ['tmax_2m4_1', 0.9734162968894661], ['tmax_2m5_1', 0.9732760733014082], ['tmp_2m_5_1', 0.971798288488127], ['tmin_2m3_1', 0.9715874768684567], ['tmin_2m2_1', 0.9709866438853475], ['tmp_2m_4_1', 0.9684774894714101], ['ulwrf_s5_1', 0.9670339151339817], ['tmp_sfc3_1', 0.9649823936827109], ['tmp_2m_1_1', 0.9638593940360212], ['tmin_2m1_1', 0.9605896048258724], ['ulwrf_s4_1', 0.9572236269499521], ['tmp_sfc1_1', 0.957188784436685], ['tmp_sfc4_1', 0.9555363809053586], ['tmax_2m1_1', 0.9528200105400535]]
[['Columna: tmp_sfc3_1'], ['ulwrf_s4_1', 0.9947627885687921], ['ulwrf_s5_1', 0.9892581590922543], ['tmp_sfc4_1', 0.9884745475700606], ['tmin_2m4_1', 0.985897294709102], ['tmax_2m4_1', 0.9857825429301692], ['tmp_2m_3_1', 0.9856020769129077], ['tmax_2m5_1', 0.9848432369308402], ['tmp_2m_4_1', 0.9838690005793208], ['tmax_2m3_1', 0.9836268315982332], ['tmin_2m5_1', 0.9704219757785946], ['ulwrf_s3_1', 0.9698096931439735], ['tmp_sfc2_1', 0.9649823936827109], ['tmp_2m_5_1', 0.9646544198978636], ['tmp_2m_2_1', 0.9570765900532514], ['tmp_sfc5_1', 0.9562755066656142], ['tmax_2m2_1', 0.9540154794130064]]
[['Columna: tmp_sfc4_1'], ['ulwrf_s5_1', 0.996612200039398], ['ulwrf_s4_1', 0.9957411309514121], ['tmp_2m_4_1', 0.9924835965861123], ['tmax_2m4_1', 0.9903005164322048], ['tmax_2m5_1', 0.9902303273948138], ['tmp_sfc3_1', 0.9884745475700606], ['tmin_2m4_1', 0.9839686317112281], ['tmin_2m5_1', 0.9799245981499726], ['tmp_2m_3_1', 0.9796642142509698], ['tmp_2m_5_1', 0.9781233238955636], ['tmax_2m3_1', 0.977121003001589], ['tmp_sfc5_1', 0.9687614236330414], ['ulwrf_s3_1', 0.9604279752910915], ['tmp_sfc2_1', 0.9555363809053586]]
[['Columna: tmp_sfc5_1'], ['tmp_2m_5_1', 0.9980075740584928], ['tmin_2m5_1', 0.9960054258735697], ['tmax_2m5_1', 0.9791492889290042], ['ulwrf_s5_1', 0.9789322162596626], ['tmp_2m_4_1', 0.9781934487046426], ['tmax_2m4_1', 0.9780961580272722], ['tmin_2m4_1', 0.9773521584222015], ['tmp_2m_3_1', 0.9758811197330793], ['tmax_2m3_1', 0.9751782371847001], ['ulwrf_s3_1', 0.9744478191517459], ['tmp_sfc2_1', 0.9740593607013978], ['tmp_2m_2_1', 0.9714528776518785], ['tmax_2m2_1', 0.9692885564165558], ['tmp_sfc4_1', 0.9687614236330414], ['ulwrf_s2_1', 0.9619766387479004], ['ulwrf_s4_1', 0.960457161005563], ['tmp_sfc3_1', 0.9562755066656142]]
[['Columna: ulwrf_s1_1'], ['tmin_2m1_1', 0.9926165091998508], ['tmax_2m1_1', 0.9925465923917536], ['tmp_sfc1_1', 0.9919281097281679], ['tmp_2m_1_1', 0.9897625110599417], ['tmin_2m2_1', 0.9850580289147287], ['tmin_2m3_1', 0.9843176374301973], ['ulwrf_s2_1', 0.9762674995860944], ['tmax_2m2_1', 0.9606784450210313], ['ulwrf_s3_1', 0.9574576992187499], ['tmp_2m_2_1', 0.9573813147342563]]
[['Columna: ulwrf_s2_1'], ['tmax_2m2_1', 0.9917542607726626], ['tmp_2m_2_1', 0.990609269111517], ['ulwrf_s3_1', 0.990564307766117], ['tmin_2m2_1', 0.9899721168841563], ['tmin_2m3_1', 0.9897318251363909], ['tmp_sfc2_1', 0.9879322271553737], ['tmp_2m_1_1', 0.9853437253651708], ['tmp_sfc1_1', 0.9842676480547997], ['tmin_2m1_1', 0.9814254257054185], ['ulwrf_s1_1', 0.9762674995860944], ['tmax_2m1_1', 0.9707221719806952], ['tmax_2m3_1', 0.966905126635995], ['tmp_2m_3_1', 0.9631310421561378], ['tmp_sfc5_1', 0.9619766387479004], ['tmin_2m4_1', 0.9587402415390521], ['tmin_2m5_1', 0.9584808725553905], ['tmp_2m_5_1', 0.9560882131646383]]
[['Columna: ulwrf_s3_1'], ['tmp_sfc2_1', 0.996855293888584], ['tmp_2m_2_1', 0.994870585784455], ['tmax_2m2_1', 0.9943559569788851], ['ulwrf_s2_1', 0.990564307766117], ['tmax_2m3_1', 0.9882838798756105], ['tmp_2m_3_1', 0.986475159576972], ['tmin_2m4_1', 0.9834305304179148], ['tmin_2m5_1', 0.9771636437810954], ['tmax_2m4_1', 0.975826585247475], ['tmax_2m5_1', 0.975536953838263], ['tmin_2m3_1', 0.9751256747221002], ['tmp_sfc5_1', 0.9744478191517459], ['tmin_2m2_1', 0.9743529570168544], ['ulwrf_s5_1', 0.9734148064192735], ['tmp_2m_5_1', 0.9731828651939985], ['tmp_2m_4_1', 0.9712226930987012], ['tmp_sfc3_1', 0.9698096931439735], ['tmp_2m_1_1', 0.968308266178652], ['tmin_2m1_1', 0.9654648681213764], ['ulwrf_s4_1', 0.9651706956885256], ['tmp_sfc1_1', 0.9621315029521358], ['tmp_sfc4_1', 0.9604279752910915], ['tmax_2m1_1', 0.9578768560091268], ['ulwrf_s1_1', 0.9574576992187499]]
[['Columna: ulwrf_s4_1'], ['ulwrf_s5_1', 0.9963430558611763], ['tmp_sfc4_1', 0.9957411309514121], ['tmp_sfc3_1', 0.9947627885687921], ['tmp_2m_4_1', 0.9875947598774877], ['tmax_2m4_1', 0.987077087459209], ['tmax_2m5_1', 0.9863521279974694], ['tmin_2m4_1', 0.9837254094808376], ['tmp_2m_3_1', 0.9807390701133252], ['tmax_2m3_1', 0.9784038012441758], ['tmin_2m5_1', 0.9737488476761204], ['tmp_2m_5_1', 0.9695091232286572], ['ulwrf_s3_1', 0.9651706956885256], ['tmp_sfc5_1', 0.960457161005563], ['tmp_sfc2_1', 0.9572236269499521]]
[['Columna: ulwrf_s5_1'], ['tmp_sfc4_1', 0.996612200039398], ['ulwrf_s4_1', 0.9963430558611763], ['tmp_2m_4_1', 0.9926662202081515], ['tmax_2m4_1', 0.9915793553887863], ['tmax_2m5_1', 0.99151293872598], ['tmp_sfc3_1', 0.9892581590922543], ['tmin_2m4_1', 0.9881522932211056], ['tmin_2m5_1', 0.9873222874147335], ['tmp_2m_5_1', 0.9852702329370912], ['tmp_2m_3_1', 0.9850287211532148], ['tmax_2m3_1', 0.9831073628529786], ['tmp_sfc5_1', 0.9789322162596626], ['ulwrf_s3_1', 0.9734148064192735], ['tmp_sfc2_1', 0.9670339151339817], ['tmp_2m_2_1', 0.960980140611558], ['tmax_2m2_1', 0.9582268665523904]]
[['Columna: ulwrf_t1_1']]
[['Columna: ulwrf_t2_1'], ['ulwrf_t3_1', 0.9744666921198298]]
[['Columna: ulwrf_t3_1'], ['ulwrf_t2_1', 0.9744666921198298]]
[['Columna: ulwrf_t4_1'], ['ulwrf_t5_1', 0.9755542908956468]]
[['Columna: ulwrf_t5_1'], ['ulwrf_t4_1', 0.9755542908956468]]
[['Columna: uswrf_s1_1']]
[['Columna: uswrf_s2_1'], ['dswrf_s2_1', 0.9911709851006711], ['dswrf_s3_1', 0.9591814530708258]]
[['Columna: uswrf_s3_1']]
[['Columna: uswrf_s4_1'], ['uswrf_s5_1', 0.9562280634672189]]
[['Columna: uswrf_s5_1'], ['uswrf_s4_1', 0.9562280634672189]]
[['Columna: salida']]
[[['Columna: apcp_sf1_1']], [['Columna: apcp_sf2_1']], [['Columna: apcp_sf3_1']], [['Columna: apcp_sf4_1']], [['Columna: apcp_sf5_1']], [['Columna: dlwrf_s1_1'], ['dlwrf_s2_1', 0.9650067922254768], ['dlwrf_s3_1', 0.9547817730760655]], [['Columna: dlwrf_s2_1'], ['dlwrf_s3_1', 0.993701215706055], ['dlwrf_s1_1', 0.9650067922254768]], [['Columna: dlwrf_s3_1'], ['dlwrf_s2_1', 0.993701215706055], ['dlwrf_s4_1', 0.9659874690575408], ['dlwrf_s5_1', 0.9552712673845433], ['dlwrf_s1_1', 0.9547817730760655]], [['Columna: dlwrf_s4_1'], ['dlwrf_s5_1', 0.9969222914149775], ['dlwrf_s3_1', 0.9659874690575408]], [['Columna: dlwrf_s5_1'], ['dlwrf_s4_1', 0.9969222914149775], ['dlwrf_s3_1', 0.9552712673845433]], [['Columna: dswrf_s1_1']], [['Columna: dswrf_s2_1'], ['uswrf_s2_1', 0.9911709851006711], ['dswrf_s3_1', 0.9503896354343679]], [['Columna: dswrf_s3_1'], ['uswrf_s2_1', 0.9591814530708258], ['dswrf_s2_1', 0.9503896354343679]], [['Columna: dswrf_s4_1'], ['dswrf_s5_1', 0.982758557897581]], [['Columna: dswrf_s5_1'], ['dswrf_s4_1', 0.982758557897581]], [['Columna: pres_ms1_1'], ['pres_ms2_1', 0.9879236602955379], ['pres_ms3_1', 0.956852960202746]], [['Columna: pres_ms2_1'], ['pres_ms1_1', 0.9879236602955379], ['pres_ms3_1', 0.9869377705171734], ['pres_ms4_1', 0.9536176398645005]], [['Columna: pres_ms3_1'], ['pres_ms2_1', 0.9869377705171734], ['pres_ms4_1', 0.9866602703072012], ['pres_ms1_1', 0.956852960202746], ['pres_ms5_1', 0.9538147697170144]], [['Columna: pres_ms4_1'], ['pres_ms3_1', 0.9866602703072012], ['pres_ms5_1', 0.9851755074525863], ['pres_ms2_1', 0.9536176398645005]], [['Columna: pres_ms5_1'], ['pres_ms4_1', 0.9851755074525863], ['pres_ms3_1', 0.9538147697170144]], [['Columna: pwat_ea1_1'], ['pwat_ea2_1', 0.9859484994851248], ['pwat_ea3_1', 0.9577107162594556]], [['Columna: pwat_ea2_1'], ['pwat_ea3_1', 0.9874259658433963], ['pwat_ea1_1', 0.9859484994851248], ['pwat_ea4_1', 0.9618712300670131]], [['Columna: pwat_ea3_1'], ['pwat_ea4_1', 0.9880603787665849], ['pwat_ea2_1', 0.9874259658433963], ['pwat_ea5_1', 0.9616424908340101], ['pwat_ea1_1', 0.9577107162594556]], [['Columna: pwat_ea4_1'], ['pwat_ea3_1', 0.9880603787665849], ['pwat_ea5_1', 0.986763801908917], ['pwat_ea2_1', 0.9618712300670131]], [['Columna: pwat_ea5_1'], ['pwat_ea4_1', 0.986763801908917], ['pwat_ea3_1', 0.9616424908340101]], [['Columna: spfh_2m1_1'], ['spfh_2m2_1', 0.9742691195680059]], [['Columna: spfh_2m2_1'], ['spfh_2m3_1', 0.9846069576918387], ['spfh_2m1_1', 0.9742691195680059], ['spfh_2m4_1', 0.9600698332225309]], [['Columna: spfh_2m3_1'], ['spfh_2m4_1', 0.9891201306737782], ['spfh_2m2_1', 0.9846069576918387], ['spfh_2m5_1', 0.9771699520274281]], [['Columna: spfh_2m4_1'], ['spfh_2m5_1', 0.9904262248914517], ['spfh_2m3_1', 0.9891201306737782], ['spfh_2m2_1', 0.9600698332225309]], [['Columna: spfh_2m5_1'], ['spfh_2m4_1', 0.9904262248914517], ['spfh_2m3_1', 0.9771699520274281]], [['Columna: tcdc_ea1_1'], ['tcolc_e1_1', 0.9999826963362115]], [['Columna: tcdc_ea2_1'], ['tcolc_e2_1', 0.9999837132775715]], [['Columna: tcdc_ea3_1'], ['tcolc_e3_1', 0.9999845616560729]], [['Columna: tcdc_ea4_1'], ['tcolc_e4_1', 0.999984785893167]], [['Columna: tcdc_ea5_1'], ['tcolc_e5_1', 0.9999746391911669]], [['Columna: tcolc_e1_1'], ['tcdc_ea1_1', 0.9999826963362115]], [['Columna: tcolc_e2_1'], ['tcdc_ea2_1', 0.9999837132775715]], [['Columna: tcolc_e3_1'], ['tcdc_ea3_1', 0.9999845616560729]], [['Columna: tcolc_e4_1'], ['tcdc_ea4_1', 0.999984785893167]], [['Columna: tcolc_e5_1'], ['tcdc_ea5_1', 0.9999746391911669]], [['Columna: tmax_2m1_1'], ['ulwrf_s1_1', 0.9925465923917536], ['tmin_2m1_1', 0.9864627566914622], ['tmp_2m_1_1', 0.9844648786308661], ['tmp_sfc1_1', 0.9826171735169642], ['tmin_2m2_1', 0.9794577781348043], ['tmin_2m3_1', 0.97879484323375], ['ulwrf_s2_1', 0.9707221719806952], ['tmax_2m2_1', 0.9637997824764319], ['tmp_2m_2_1', 0.9602996922677912], ['ulwrf_s3_1', 0.9578768560091268], ['tmp_sfc2_1', 0.9528200105400535]], [['Columna: tmax_2m2_1'], ['tmp_2m_2_1', 0.9993112900738094], ['tmp_sfc2_1', 0.9963595768447152], ['ulwrf_s3_1', 0.9943559569788851], ['ulwrf_s2_1', 0.9917542607726626], ['tmax_2m3_1', 0.9863610206089146], ['tmp_2m_3_1', 0.9829472636912582], ['tmin_2m2_1', 0.9819461458841241], ['tmin_2m3_1', 0.9819334574863472], ['tmin_2m4_1', 0.9790285687297058], ['tmp_2m_1_1', 0.9771397511742945], ['tmin_2m1_1', 0.9735093237780129], ['tmin_2m5_1', 0.9713195080332866], ['tmax_2m4_1', 0.9698857690834339], ['tmax_2m5_1', 0.9697541323437304], ['tmp_sfc1_1', 0.9697293778574791], ['tmp_sfc5_1', 0.9692885564165558], ['tmp_2m_5_1', 0.9675902215544175], ['tmp_2m_4_1', 0.9645870257681579], ['tmax_2m1_1', 0.9637997824764319], ['ulwrf_s1_1', 0.9606784450210313], ['ulwrf_s5_1', 0.9582268665523904], ['tmp_sfc3_1', 0.9540154794130064]], [['Columna: tmax_2m3_1'], ['tmp_2m_3_1', 0.9989979986044517], ['tmin_2m4_1', 0.9973034556239585], ['tmax_2m4_1', 0.9937711137699543], ['tmax_2m5_1', 0.9931925682521422], ['tmp_2m_4_1', 0.9902875444682745], ['ulwrf_s3_1', 0.9882838798756105], ['tmp_2m_2_1', 0.9880399268817291], ['tmp_sfc2_1', 0.9874628996347063], ['tmax_2m2_1', 0.9863610206089146], ['tmin_2m5_1', 0.9849440628695366], ['tmp_sfc3_1', 0.9836268315982332], ['ulwrf_s5_1', 0.9831073628529786], ['tmp_2m_5_1', 0.9800734262132967], ['ulwrf_s4_1', 0.9784038012441758], ['tmp_sfc4_1', 0.977121003001589], ['tmp_sfc5_1', 0.9751782371847001], ['ulwrf_s2_1', 0.966905126635995], ['tmin_2m3_1', 0.9565532246066281], ['tmin_2m2_1', 0.9554925734198499]], [['Columna: tmax_2m4_1'], ['tmax_2m5_1', 0.999855391919824], ['tmp_2m_4_1', 0.9989670084606509], ['tmin_2m4_1', 0.9965267117336937], ['tmp_2m_3_1', 0.9954824569443925], ['tmax_2m3_1', 0.9937711137699543], ['ulwrf_s5_1', 0.9915793553887863], ['tmp_sfc4_1', 0.9903005164322048], ['tmin_2m5_1', 0.9888950622930037], ['ulwrf_s4_1', 0.987077087459209], ['tmp_2m_5_1', 0.9862698111992504], ['tmp_sfc3_1', 0.9857825429301692], ['tmp_sfc5_1', 0.9780961580272722], ['ulwrf_s3_1', 0.975826585247475], ['tmp_sfc2_1', 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0.9884708317878447], ['ulwrf_s5_1', 0.9873222874147335], ['tmp_2m_3_1', 0.9863630955598195], ['tmax_2m3_1', 0.9849440628695366], ['tmp_sfc4_1', 0.9799245981499726], ['ulwrf_s3_1', 0.9771636437810954], ['tmp_sfc2_1', 0.975307217691161], ['tmp_2m_2_1', 0.973997775172227], ['ulwrf_s4_1', 0.9737488476761204], ['tmax_2m2_1', 0.9713195080332866], ['tmp_sfc3_1', 0.9704219757785946], ['ulwrf_s2_1', 0.9584808725553905]], [['Columna: tmp_2m_1_1'], ['tmin_2m2_1', 0.9981819106379802], ['tmin_2m1_1', 0.9981185076252812], ['tmp_sfc1_1', 0.9979448017549044], ['tmin_2m3_1', 0.9973859703817012], ['ulwrf_s1_1', 0.9897625110599417], ['ulwrf_s2_1', 0.9853437253651708], ['tmax_2m1_1', 0.9844648786308661], ['tmax_2m2_1', 0.9771397511742945], ['tmp_2m_2_1', 0.9744909003873826], ['ulwrf_s3_1', 0.968308266178652], ['tmp_sfc2_1', 0.9638593940360212]], [['Columna: tmp_2m_2_1'], ['tmax_2m2_1', 0.9993112900738094], ['tmp_sfc2_1', 0.9971658992806404], ['ulwrf_s3_1', 0.994870585784455], ['ulwrf_s2_1', 0.990609269111517], ['tmax_2m3_1', 0.9880399268817291], ['tmp_2m_3_1', 0.9856921834337115], ['tmin_2m4_1', 0.9819192741530762], ['tmin_2m3_1', 0.9813049765366397], ['tmin_2m2_1', 0.9808603480091717], ['tmp_2m_1_1', 0.9744909003873826], ['tmin_2m5_1', 0.973997775172227], ['tmax_2m4_1', 0.9730783300205592], ['tmax_2m5_1', 0.9729567511523166], ['tmp_sfc5_1', 0.9714528776518785], ['tmin_2m1_1', 0.9708467193246784], ['tmp_2m_5_1', 0.9702697100229983], ['tmp_2m_4_1', 0.9679046544965666], ['tmp_sfc1_1', 0.9668200402557023], ['ulwrf_s5_1', 0.960980140611558], ['tmax_2m1_1', 0.9602996922677912], ['ulwrf_s1_1', 0.9573813147342563], ['tmp_sfc3_1', 0.9570765900532514]], [['Columna: tmp_2m_3_1'], ['tmax_2m3_1', 0.9989979986044517], ['tmin_2m4_1', 0.9989480053798309], ['tmax_2m4_1', 0.9954824569443925], ['tmax_2m5_1', 0.9948623637802105], ['tmp_2m_4_1', 0.9925508354894933], ['ulwrf_s3_1', 0.986475159576972], ['tmin_2m5_1', 0.9863630955598195], ['tmp_2m_2_1', 0.9856921834337115], ['tmp_sfc3_1', 0.9856020769129077], ['tmp_sfc2_1', 0.9853721430018374], ['ulwrf_s5_1', 0.9850287211532148], ['tmax_2m2_1', 0.9829472636912582], ['tmp_2m_5_1', 0.9813875596975576], ['ulwrf_s4_1', 0.9807390701133252], ['tmp_sfc4_1', 0.9796642142509698], ['tmp_sfc5_1', 0.9758811197330793], ['ulwrf_s2_1', 0.9631310421561378], ['tmin_2m3_1', 0.9537926818714694], ['tmin_2m2_1', 0.9516543862813964]], [['Columna: tmp_2m_4_1'], ['tmax_2m4_1', 0.9989670084606509], ['tmax_2m5_1', 0.9988753498975144], ['tmin_2m4_1', 0.9955546180266533], ['ulwrf_s5_1', 0.9926662202081515], ['tmp_2m_3_1', 0.9925508354894933], ['tmp_sfc4_1', 0.9924835965861123], ['tmax_2m3_1', 0.9902875444682745], ['tmin_2m5_1', 0.9895327574093381], ['ulwrf_s4_1', 0.9875947598774877], ['tmp_2m_5_1', 0.9871404965220815], ['tmp_sfc3_1', 0.9838690005793208], ['tmp_sfc5_1', 0.9781934487046426], ['ulwrf_s3_1', 0.9712226930987012], ['tmp_sfc2_1', 0.9684774894714101], ['tmp_2m_2_1', 0.9679046544965666], ['tmax_2m2_1', 0.9645870257681579]], [['Columna: tmp_2m_5_1'], ['tmin_2m5_1', 0.9985248666465218], ['tmp_sfc5_1', 0.9980075740584928], ['tmax_2m5_1', 0.9872536923433799], ['tmp_2m_4_1', 0.9871404965220815], ['tmax_2m4_1', 0.9862698111992504], ['ulwrf_s5_1', 0.9852702329370912], ['tmin_2m4_1', 0.9836800034039297], ['tmp_2m_3_1', 0.9813875596975576], ['tmax_2m3_1', 0.9800734262132967], ['tmp_sfc4_1', 0.9781233238955636], ['ulwrf_s3_1', 0.9731828651939985], ['tmp_sfc2_1', 0.971798288488127], ['tmp_2m_2_1', 0.9702697100229983], ['ulwrf_s4_1', 0.9695091232286572], ['tmax_2m2_1', 0.9675902215544175], ['tmp_sfc3_1', 0.9646544198978636], ['ulwrf_s2_1', 0.9560882131646383]], [['Columna: tmp_sfc1_1'], ['tmp_2m_1_1', 0.9979448017549044], ['tmin_2m1_1', 0.9963142972792025], ['tmin_2m2_1', 0.9960749292152973], ['tmin_2m3_1', 0.9951903927420194], ['ulwrf_s1_1', 0.9919281097281679], ['ulwrf_s2_1', 0.9842676480547997], ['tmax_2m1_1', 0.9826171735169642], ['tmax_2m2_1', 0.9697293778574791], ['tmp_2m_2_1', 0.9668200402557023], ['ulwrf_s3_1', 0.9621315029521358], ['tmp_sfc2_1', 0.957188784436685]], [['Columna: tmp_sfc2_1'], ['tmp_2m_2_1', 0.9971658992806404], ['ulwrf_s3_1', 0.996855293888584], ['tmax_2m2_1', 0.9963595768447152], ['ulwrf_s2_1', 0.9879322271553737], ['tmax_2m3_1', 0.9874628996347063], ['tmp_2m_3_1', 0.9853721430018374], ['tmin_2m4_1', 0.9818609853360799], ['tmin_2m5_1', 0.975307217691161], ['tmp_sfc5_1', 0.9740593607013978], ['tmax_2m4_1', 0.9734162968894661], ['tmax_2m5_1', 0.9732760733014082], ['tmp_2m_5_1', 0.971798288488127], ['tmin_2m3_1', 0.9715874768684567], ['tmin_2m2_1', 0.9709866438853475], ['tmp_2m_4_1', 0.9684774894714101], ['ulwrf_s5_1', 0.9670339151339817], ['tmp_sfc3_1', 0.9649823936827109], ['tmp_2m_1_1', 0.9638593940360212], ['tmin_2m1_1', 0.9605896048258724], ['ulwrf_s4_1', 0.9572236269499521], ['tmp_sfc1_1', 0.957188784436685], ['tmp_sfc4_1', 0.9555363809053586], ['tmax_2m1_1', 0.9528200105400535]], [['Columna: tmp_sfc3_1'], ['ulwrf_s4_1', 0.9947627885687921], ['ulwrf_s5_1', 0.9892581590922543], ['tmp_sfc4_1', 0.9884745475700606], ['tmin_2m4_1', 0.985897294709102], ['tmax_2m4_1', 0.9857825429301692], ['tmp_2m_3_1', 0.9856020769129077], ['tmax_2m5_1', 0.9848432369308402], ['tmp_2m_4_1', 0.9838690005793208], ['tmax_2m3_1', 0.9836268315982332], ['tmin_2m5_1', 0.9704219757785946], ['ulwrf_s3_1', 0.9698096931439735], ['tmp_sfc2_1', 0.9649823936827109], ['tmp_2m_5_1', 0.9646544198978636], ['tmp_2m_2_1', 0.9570765900532514], ['tmp_sfc5_1', 0.9562755066656142], ['tmax_2m2_1', 0.9540154794130064]], [['Columna: tmp_sfc4_1'], ['ulwrf_s5_1', 0.996612200039398], ['ulwrf_s4_1', 0.9957411309514121], ['tmp_2m_4_1', 0.9924835965861123], ['tmax_2m4_1', 0.9903005164322048], ['tmax_2m5_1', 0.9902303273948138], ['tmp_sfc3_1', 0.9884745475700606], ['tmin_2m4_1', 0.9839686317112281], ['tmin_2m5_1', 0.9799245981499726], ['tmp_2m_3_1', 0.9796642142509698], ['tmp_2m_5_1', 0.9781233238955636], ['tmax_2m3_1', 0.977121003001589], ['tmp_sfc5_1', 0.9687614236330414], ['ulwrf_s3_1', 0.9604279752910915], ['tmp_sfc2_1', 0.9555363809053586]], [['Columna: tmp_sfc5_1'], ['tmp_2m_5_1', 0.9980075740584928], ['tmin_2m5_1', 0.9960054258735697], ['tmax_2m5_1', 0.9791492889290042], ['ulwrf_s5_1', 0.9789322162596626], ['tmp_2m_4_1', 0.9781934487046426], ['tmax_2m4_1', 0.9780961580272722], ['tmin_2m4_1', 0.9773521584222015], ['tmp_2m_3_1', 0.9758811197330793], ['tmax_2m3_1', 0.9751782371847001], ['ulwrf_s3_1', 0.9744478191517459], ['tmp_sfc2_1', 0.9740593607013978], ['tmp_2m_2_1', 0.9714528776518785], ['tmax_2m2_1', 0.9692885564165558], ['tmp_sfc4_1', 0.9687614236330414], ['ulwrf_s2_1', 0.9619766387479004], ['ulwrf_s4_1', 0.960457161005563], ['tmp_sfc3_1', 0.9562755066656142]], [['Columna: ulwrf_s1_1'], ['tmin_2m1_1', 0.9926165091998508], ['tmax_2m1_1', 0.9925465923917536], ['tmp_sfc1_1', 0.9919281097281679], ['tmp_2m_1_1', 0.9897625110599417], ['tmin_2m2_1', 0.9850580289147287], ['tmin_2m3_1', 0.9843176374301973], ['ulwrf_s2_1', 0.9762674995860944], ['tmax_2m2_1', 0.9606784450210313], ['ulwrf_s3_1', 0.9574576992187499], ['tmp_2m_2_1', 0.9573813147342563]], [['Columna: ulwrf_s2_1'], ['tmax_2m2_1', 0.9917542607726626], ['tmp_2m_2_1', 0.990609269111517], ['ulwrf_s3_1', 0.990564307766117], ['tmin_2m2_1', 0.9899721168841563], ['tmin_2m3_1', 0.9897318251363909], ['tmp_sfc2_1', 0.9879322271553737], ['tmp_2m_1_1', 0.9853437253651708], ['tmp_sfc1_1', 0.9842676480547997], ['tmin_2m1_1', 0.9814254257054185], ['ulwrf_s1_1', 0.9762674995860944], ['tmax_2m1_1', 0.9707221719806952], ['tmax_2m3_1', 0.966905126635995], ['tmp_2m_3_1', 0.9631310421561378], ['tmp_sfc5_1', 0.9619766387479004], ['tmin_2m4_1', 0.9587402415390521], ['tmin_2m5_1', 0.9584808725553905], ['tmp_2m_5_1', 0.9560882131646383]], [['Columna: ulwrf_s3_1'], ['tmp_sfc2_1', 0.996855293888584], ['tmp_2m_2_1', 0.994870585784455], ['tmax_2m2_1', 0.9943559569788851], ['ulwrf_s2_1', 0.990564307766117], ['tmax_2m3_1', 0.9882838798756105], ['tmp_2m_3_1', 0.986475159576972], ['tmin_2m4_1', 0.9834305304179148], ['tmin_2m5_1', 0.9771636437810954], ['tmax_2m4_1', 0.975826585247475], ['tmax_2m5_1', 0.975536953838263], ['tmin_2m3_1', 0.9751256747221002], ['tmp_sfc5_1', 0.9744478191517459], ['tmin_2m2_1', 0.9743529570168544], ['ulwrf_s5_1', 0.9734148064192735], ['tmp_2m_5_1', 0.9731828651939985], ['tmp_2m_4_1', 0.9712226930987012], ['tmp_sfc3_1', 0.9698096931439735], ['tmp_2m_1_1', 0.968308266178652], ['tmin_2m1_1', 0.9654648681213764], ['ulwrf_s4_1', 0.9651706956885256], ['tmp_sfc1_1', 0.9621315029521358], ['tmp_sfc4_1', 0.9604279752910915], ['tmax_2m1_1', 0.9578768560091268], ['ulwrf_s1_1', 0.9574576992187499]], [['Columna: ulwrf_s4_1'], ['ulwrf_s5_1', 0.9963430558611763], ['tmp_sfc4_1', 0.9957411309514121], ['tmp_sfc3_1', 0.9947627885687921], ['tmp_2m_4_1', 0.9875947598774877], ['tmax_2m4_1', 0.987077087459209], ['tmax_2m5_1', 0.9863521279974694], ['tmin_2m4_1', 0.9837254094808376], ['tmp_2m_3_1', 0.9807390701133252], ['tmax_2m3_1', 0.9784038012441758], ['tmin_2m5_1', 0.9737488476761204], ['tmp_2m_5_1', 0.9695091232286572], ['ulwrf_s3_1', 0.9651706956885256], ['tmp_sfc5_1', 0.960457161005563], ['tmp_sfc2_1', 0.9572236269499521]], [['Columna: ulwrf_s5_1'], ['tmp_sfc4_1', 0.996612200039398], ['ulwrf_s4_1', 0.9963430558611763], ['tmp_2m_4_1', 0.9926662202081515], ['tmax_2m4_1', 0.9915793553887863], ['tmax_2m5_1', 0.99151293872598], ['tmp_sfc3_1', 0.9892581590922543], ['tmin_2m4_1', 0.9881522932211056], ['tmin_2m5_1', 0.9873222874147335], ['tmp_2m_5_1', 0.9852702329370912], ['tmp_2m_3_1', 0.9850287211532148], ['tmax_2m3_1', 0.9831073628529786], ['tmp_sfc5_1', 0.9789322162596626], ['ulwrf_s3_1', 0.9734148064192735], ['tmp_sfc2_1', 0.9670339151339817], ['tmp_2m_2_1', 0.960980140611558], ['tmax_2m2_1', 0.9582268665523904]], [['Columna: ulwrf_t1_1']], [['Columna: ulwrf_t2_1'], ['ulwrf_t3_1', 0.9744666921198298]], [['Columna: ulwrf_t3_1'], ['ulwrf_t2_1', 0.9744666921198298]], [['Columna: ulwrf_t4_1'], ['ulwrf_t5_1', 0.9755542908956468]], [['Columna: ulwrf_t5_1'], ['ulwrf_t4_1', 0.9755542908956468]], [['Columna: uswrf_s1_1']], [['Columna: uswrf_s2_1'], ['dswrf_s2_1', 0.9911709851006711], ['dswrf_s3_1', 0.9591814530708258]], [['Columna: uswrf_s3_1']], [['Columna: uswrf_s4_1'], ['uswrf_s5_1', 0.9562280634672189]], [['Columna: uswrf_s5_1'], ['uswrf_s4_1', 0.9562280634672189]], [['Columna: salida']]]


""" seaborne Correlation Heat Map """
# It needs to show all the columns
fig, ax = plt.subplots(figsize=(19,18))

plt.title('Correlation Heat Map',y=1)
# We use blue color scale because it is easier to see the annotations and the correlation values
sns.heatmap(correlation, square=True, cmap='Blues', annot=True, fmt='.2f', annot_kws={'size': 4}, cbar_kws={"shrink": .5}, vmin=0.0, vmax=1)
# We can modify vmax=0.95 in order to get same color scale for values with more than 0.95 correlation
# Note: it delays around 15 seconds as it needs to plot a 76*76 matrix with its 5766 correlation values

# Exporting image as png to ../data/img folder - easier to visualize the annotations, better resolution
plt.savefig("../data/img/correlation_heatmap.png", dpi=200)


# 1
columns = ['apcp_sf1_1', 'apcp_sf2_1', 'apcp_sf3_1','apcp_sf4_1', 'apcp_sf5_1']

sns.pairplot(train_df[columns], height = 1, kind ='scatter',diag_kind='kde')
plt.show()


# 2
columns = [ 'dlwrf_s1_1', 'dlwrf_s2_1', 'dlwrf_s3_1', 'dlwrf_s4_1', 'dlwrf_s5_1']

sns.pairplot(train_df[columns], height = 1, kind ='scatter',diag_kind='kde')
plt.show()


# 3
columns = ['pwat_ea1_1', 'pwat_ea2_1','pwat_ea3_1','pwat_ea4_1','pwat_ea5_1', 'dlwrf_s1_1', 'dlwrf_s2_1', 'dlwrf_s3_1', 'dlwrf_s4_1', 'dlwrf_s5_1']

sns.pairplot(train_df[columns], height = 1, kind ='scatter',diag_kind='kde')
plt.show()


# 4
columns = ['dswrf_s1_1', 'dswrf_s2_1', 'dswrf_s3_1', 'dswrf_s4_1', 'dswrf_s5_1']

sns.pairplot(train_df[columns], height = 1, kind ='scatter',diag_kind='kde')
plt.show()


# 5
columns = ['dswrf_s1_1', 'dswrf_s2_1', 'dswrf_s3_1', 'dswrf_s4_1', 'dswrf_s5_1', 'uswrf_s1_1', 'uswrf_s2_1', 'uswrf_s3_1', 'uswrf_s4_1', 'uswrf_s5_1']

sns.pairplot(train_df[columns], height = 1, kind ='scatter',diag_kind='kde')
plt.show()


# 6
columns = ['pres_ms1_1', 'pres_ms2_1', 'pres_ms3_1', 'pres_ms4_1', 'pres_ms5_1']

sns.pairplot(train_df[columns], height = 1, kind ='scatter',diag_kind='kde')
plt.show()


# 7
columns = ['pwat_ea1_1', 'pwat_ea2_1','pwat_ea3_1','pwat_ea4_1','pwat_ea5_1', 'spfh_2m1_1', 'spfh_2m2_1', 'spfh_2m3_1', 'spfh_2m4_1', 'spfh_2m5_1']

sns.pairplot(train_df[columns], height = 1, kind ='scatter',diag_kind='kde')
plt.show()


# 8
columns = ['spfh_2m1_1', 'spfh_2m2_1', 'spfh_2m3_1', 'spfh_2m4_1', 'spfh_2m5_1','ulwrf_s1_1', 'ulwrf_s2_1', 'ulwrf_s3_1', 'ulwrf_s4_1', 'ulwrf_s5_1']

sns.pairplot(train_df[columns], height = 1, kind ='scatter',diag_kind='kde')
plt.show()


# 9
# columns = ['tmax_2m1_1', 'tmax_2m2_1', 'tmax_2m3_1', 'tmax_2m4_1', 'tmax_2m5_1', 'tmin_2m1_1', 'tmin_2m2_1', 'tmin_2m3_1', 'tmin_2m4_1', 'tmin_2m5_1','tmp_2m_1_1', 'tmp_2m_2_1', 'tmp_2m_3_1', 'tmp_2m_4_1', 'tmp_2m_5_1', 'tmp_sfc1_1', 'tmp_sfc2_1', 'tmp_sfc3_1', 'tmp_sfc4_1', 'tmp_sfc5_1','ulwrf_s1_1', 'ulwrf_s2_1', 'ulwrf_s3_1', 'ulwrf_s4_1', 'ulwrf_s5_1']

# sns.pairplot(train_df[columns], height = 1 ,kind ='scatter',diag_kind='kde')
# plt.show()


# 10
columns = ['ulwrf_t1_1', 'ulwrf_t2_1', 'ulwrf_t3_1']

sns.pairplot(train_df[columns], height = 1, kind ='scatter',diag_kind='kde')
plt.show()


# 11
columns = ['ulwrf_t4_1', 'ulwrf_t5_1', ]

sns.pairplot(train_df[columns], height = 1 ,kind ='scatter',diag_kind='kde')
plt.show()


# 12
columns = ['uswrf_s2_1', 'uswrf_s3_1', 'uswrf_s4_1', 'uswrf_s5_1']

sns.pairplot(train_df[columns], height = 1 ,kind ='scatter',diag_kind='kde')
plt.show()


import time
import matplotlib.pyplot as plt 

# Import the metrics from sklearn
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

from sklearn.pipeline import Pipeline

# As we have noted during the EDA, for this dataset full of outliers, its preferable to use the RobustScaler
# Although this wont make a huge difference
from sklearn.preprocessing import StandardScaler, RobustScaler

from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.model_selection import TimeSeriesSplit, RandomizedSearchCV, GridSearchCV


""" Train Test Split (time series) """

np.random.seed(10)

# * Make a copy of the dataframe (as Padas dataframe is mutable, therefore uses a reference)
disp_df_copy = disp_df.copy()

# print(disp_df)
# print(disp_df_copy)

# Now we make the train_x, train_y, test_x, test_y splits taking into account the time series
# Note: the time series is ordered by date, therefore we need to split the data in a way that the train data is before the test data
# Note: the 10 first years are used for training and the last two years for testing
# Note: this is done because if not, we will be predicting the past from the future, which leads to errors and overfitting (data leakage) in the model

# * Calculate the number of rows for training and testing
num_rows = disp_df_copy.shape[0]
num_train_rows = int(num_rows * 10/12)  # 10 first years for training, 2 last years for testing

# * Split the data into train and test dataframes (using iloc instead of train_test_split as it picks random rows)
train_df = disp_df_copy.iloc[:num_train_rows, :]  # train contains the first 10 years of rows
test_df = disp_df_copy.iloc[num_train_rows:, :] # test contains the last 2 years of rows

# Print the number of rows for each dataframe
print(f"Number of rows for training: {train_df.shape[0]}")
print(f"Number of rows for testing: {test_df.shape[0]}")

# Print the dataframes
# print(train_df), print(test_df)

# * Separate the input features and target variable for training and testing
X_train = train_df.drop('salida', axis=1)   # This is the input features for training
y_train = train_df['salida']                # This is the target variable for training
X_test = test_df.drop('salida', axis=1)     # This is the input features for testing
y_test = test_df['salida']                  # This is the target variable for testing

# Print the shapes of the dataframes
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

Number of rows for training: 3650
Number of rows for testing: 730
(3650, 75) (3650,) (730, 75) (730,)


np.random.seed(10)

def train_and_test(model, X_train, y_train, X_test, y_test):
    model.fit(X=X_train,y=y_train)
    y_train_pred = model.predict(X_train)
    y_test_pred = model.predict(X_test)
    
    # RMSE
    rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)
    rmse_test = mean_squared_error(y_test, y_test_pred, squared=False)
    
    # MAE
    mae_train = mean_absolute_error(y_train, y_train_pred)
    mae_test = mean_absolute_error(y_test, y_test_pred)
    
    print(f"Results of the best estimator of {model.__class__.__name__}")
    print(f"RMSE train: {rmse_train:.2f}", f"RMSE test: {rmse_test:.2f}", sep='\t')
    print(f"MAE train: {mae_train:.2f}", f"MAE test: {mae_test:.2f}", sep='\t')
    
    plt.plot(X_train.iloc[:,[0]], y_train, '.', label='train')
    plt.plot(X_train.iloc[:,[0]], y_train_pred, 'r.', label='train pred')
    plt.legend()
    plt.show()
    
    return [rmse_train, rmse_test, mae_train, mae_test]


def print_results(name, model, score, time):
    print("---------------------------------------------------")
    print(f"{name} best model is:\n\n{model}")
    print("\nParameters:", model.best_params_)
    print(f"\nPerformance: RMSE train: {score[0]}", f"RMSE test: {score[1]}", f"MAE train: {score[2]}", f"MAE test: {score[3]}", sep=' | ')
    print(f"Execution time: {time}s")


# Two dictionaries to store the results of the models
results, times = {}, {}


from sklearn.neighbors import KNeighborsRegressor


np.random.seed(10)
n_splits = 5

# Using a pipeline to scale the data and then apply the model
pipeline = Pipeline([
    ('scale', StandardScaler()),
    # ('select', SelectKBest(f_regression)),
    ('model', KNeighborsRegressor())
])

param_grid = {
    'model__n_neighbors': [5],
    'model__weights': ['uniform'],
    'model__metric': ['minkowski'],
    # 'select__k': list(range(1, X_train.shape[1]+1))
}

regr = GridSearchCV(pipeline, 
                         param_grid,
                         scoring='neg_mean_absolute_error',
                         cv=TimeSeriesSplit(n_splits),  # We use TimeSeriesSplit to split the data in folds without losing the temporal order
                        )

start_time = time.time()
regr.fit(X=X_train,y=y_train)
end_time = time.time()

total_time = end_time - start_time

score = train_and_test(regr.best_estimator_, X_train, y_train, X_test, y_test) # We train and test the model another time to get the final score (with the best parameters)

results['KNN_pred'] = score
times['KNN_pred'] = total_time

print_results("KNN PREDEFINED PARAMETERS", regr, score, total_time)

Results of the best estimator of Pipeline
RMSE train: 2995411.40	RMSE test: 3669004.35
MAE train: 2105354.33	MAE test: 2589913.23

---------------------------------------------------
KNN PREDEFINED PARAMETERS best model is:

GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),
             estimator=Pipeline(steps=[('scale', StandardScaler()),
                                       ('model', KNeighborsRegressor())]),
             param_grid={'model__metric': ['minkowski'],
                         'model__n_neighbors': [5],
                         'model__weights': ['uniform']},
             scoring='neg_mean_absolute_error')

Parameters: {'model__metric': 'minkowski', 'model__n_neighbors': 5, 'model__weights': 'uniform'}

Performance: RMSE train: 2995411.3954362 | RMSE test: 3669004.351737694 | MAE train: 2105354.3342465754 | MAE test: 2589913.2328767125
Execution time: 0.18954920768737793s


np.random.seed(10)
budget = 15
n_splits = 5

pipeline = Pipeline([
    ('scaler', RobustScaler()), 
    ('select', SelectKBest(f_regression)),
    ('model', KNeighborsRegressor())
])

param_grid = {'model__n_neighbors': list(range(1, 50, 2)),
              'model__weights': ['uniform', 'distance'],
              'model__metric': ['euclidean', 'manhattan', 'minkowski','chebyshev'],
              'select__k': list(range(1, X_train.shape[1]+1))
              }

regr = RandomizedSearchCV(pipeline, 
                         param_grid,
                         scoring='neg_mean_absolute_error', 
                         cv=TimeSeriesSplit(n_splits), # TimeSeriesSplit to split the data in folds without losing the temporal order
                         n_iter=budget
                        )

start_time = time.time()
regr.fit(X=X_train,y=y_train)
end_time = time.time()

total_time = end_time - start_time

score = train_and_test(regr.best_estimator_, X_train, y_train, X_test, y_test) # We train and test the model another time to get the final score (with the best parameters)

results['KNN_select'] = score
times['KNN_select'] = total_time

print_results("KNN SELECTED PARAMETERS", regr, score, total_time)

Results of the best estimator of Pipeline
RMSE train: 3386909.05	RMSE test: 3394685.60
MAE train: 2410845.29	MAE test: 2420543.42

---------------------------------------------------
KNN SELECTED PARAMETERS best model is:

RandomizedSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),
                   estimator=Pipeline(steps=[('scaler', RobustScaler()),
                                             ('select',
                                              SelectKBest(score_func=<function f_regression at 0x7feb48c353f0>)),
                                             ('model', KNeighborsRegressor())]),
                   n_iter=15,
                   param_distributions={'model__metric': ['euclidean',
                                                          'manhattan',
                                                          'minkowski',
                                                          'chebyshev'],
                                        'model__n_neighbors': [1, 3, 5, 7, 9,
                                                               11, 13, 15, 17,
                                                               19, 21, 23, 25,
                                                               27, 29, 31, 33,
                                                               35, 37, 39, 41,
                                                               43, 45, 47, 49],
                                        'model__weights': ['uniform',
                                                           'distance'],
                                        'select__k': [1, 2, 3, 4, 5, 6, 7, 8, 9,
                                                      10, 11, 12, 13, 14, 15,
                                                      16, 17, 18, 19, 20, 21,
                                                      22, 23, 24, 25, 26, 27,
                                                      28, 29, 30, ...]},
                   scoring='neg_mean_absolute_error')

Parameters: {'select__k': 30, 'model__weights': 'uniform', 'model__n_neighbors': 15, 'model__metric': 'manhattan'}

Performance: RMSE train: 3386909.0476090116 | RMSE test: 3394685.600663936 | MAE train: 2410845.287671233 | MAE test: 2420543.4246575343
Execution time: 2.0830042362213135s


from sklearn.tree import DecisionTreeRegressor


np.random.seed(10)
n_splits = 5

pipeline = Pipeline([
    # ('select', SelectKBest(f_regression)),
    ('model', DecisionTreeRegressor())
])

param_grid = {'model__criterion' : ["squared_error"],
              'model__splitter' : ["best"],              
              'model__min_samples_split' : [2], }
            #   'select__k': list(range(1, X_train.shape[1]+1))}

# We use TimeSeriesSplit to split the data in folds without losing the temporal order
regr = GridSearchCV(     pipeline, 
                         param_grid,
                         scoring='neg_mean_absolute_error',
                         cv=TimeSeriesSplit(n_splits), 
                        )

start_time = time.time()
regr.fit(X=X_train,y=y_train)
end_time = time.time()

total_time = end_time - start_time

score = train_and_test(regr.best_estimator_, X_train, y_train, X_test, y_test) # We train and test the model another time to get the final score (with the best parameters)

results['RegTrees_pred'] = score
times['RegTrees_pred'] = total_time

print_results("REGRESSION TREES PREDEFINED PARAMETERS", regr, score, total_time)

Results of the best estimator of Pipeline
RMSE train: 0.00	RMSE test: 4413372.50
MAE train: 0.00	MAE test: 3136761.37

---------------------------------------------------
REGRESSION TREES PREDEFINED PARAMETERS best model is:

GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),
             estimator=Pipeline(steps=[('model', DecisionTreeRegressor())]),
             param_grid={'model__criterion': ['squared_error'],
                         'model__min_samples_split': [2],
                         'model__splitter': ['best']},
             scoring='neg_mean_absolute_error')

Parameters: {'model__criterion': 'squared_error', 'model__min_samples_split': 2, 'model__splitter': 'best'}

Performance: RMSE train: 0.0 | RMSE test: 4413372.497795917 | MAE train: 0.0 | MAE test: 3136761.3698630137
Execution time: 0.6608903408050537s


np.random.seed(10)
budget = 15
n_splits = 5

pipeline = Pipeline([
    ('select', SelectKBest(f_regression)),
    ('model', DecisionTreeRegressor())
])

param_grid = {'model__criterion' : ["absolute_error", "squared_error"],
              'model__splitter' : ["best", "random"],
              'model__max_depth' : list(range(5, 60, 5)),
              'model__min_samples_split' : list(range(5, 200)),
              'model__max_features' : ["sqrt", "log2", None], 
              'select__k': list(range(1, X_train.shape[1]+1))}

# We use TimeSeriesSplit to split the data in folds without losing the temporal order
regr = RandomizedSearchCV(pipeline, 
                         param_grid,
                         scoring='neg_mean_absolute_error',
                         cv=TimeSeriesSplit(n_splits),
                         n_iter=budget
)

start_time = time.time()                      
regr.fit(X=X_train,y=y_train)
end_time = time.time()

total_time =  end_time - start_time

score = train_and_test(regr.best_estimator_, X_train, y_train, X_test, y_test) # We train and test the model another time to get the final score (with the best parameters)

results['RegTrees_select'] = score
times['RegTrees_select'] = total_time

print_results("REGRESSION TREES SELECTED PARAMETERS", regr, score, total_time)

Results of the best estimator of Pipeline
RMSE train: 3483153.61	RMSE test: 3691730.92
MAE train: 2250097.48	MAE test: 2489240.55

---------------------------------------------------
REGRESSION TREES SELECTED PARAMETERS best model is:

RandomizedSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),
                   estimator=Pipeline(steps=[('select',
                                              SelectKBest(score_func=<function f_regression at 0x7feb48c353f0>)),
                                             ('model',
                                              DecisionTreeRegressor())]),
                   n_iter=15,
                   param_distributions={'model__criterion': ['absolute_error',
                                                             'squared_error'],
                                        'model__max_depth': [5, 10, 15, 20, 25,
                                                             30, 35, 40, 45, 50,
                                                             55],
                                        'model__max_features': ['sqrt', 'log2',
                                                                None],
                                        'model__min_samples_split': [5, 6, 7, 8,
                                                                     9, 10, 11,
                                                                     12, 13, 14,
                                                                     15, 16, 17,
                                                                     18, 19, 20,
                                                                     21, 22, 23,
                                                                     24, 25, 26,
                                                                     27, 28, 29,
                                                                     30, 31, 32,
                                                                     33, 34, ...],
                                        'model__splitter': ['best', 'random'],
                                        'select__k': [1, 2, 3, 4, 5, 6, 7, 8, 9,
                                                      10, 11, 12, 13, 14, 15,
                                                      16, 17, 18, 19, 20, 21,
                                                      22, 23, 24, 25, 26, 27,
                                                      28, 29, 30, ...]},
                   scoring='neg_mean_absolute_error')

Parameters: {'select__k': 57, 'model__splitter': 'best', 'model__min_samples_split': 168, 'model__max_features': None, 'model__max_depth': 55, 'model__criterion': 'absolute_error'}

Performance: RMSE train: 3483153.6109944335 | RMSE test: 3691730.92222464 | MAE train: 2250097.479452055 | MAE test: 2489240.5479452056
Execution time: 7.55217170715332s


from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet


np.random.seed(10)
n_splits=5

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    # ('select', SelectKBest(f_regression)),
    ('model', LinearRegression())
])

param_grid = {'model__fit_intercept': [True], }
            #   'select__k': list(range(1, X_train.shape[1]+1))}

regr = GridSearchCV(pipeline,
                    param_grid,
                    scoring='neg_mean_absolute_error',
                    cv=TimeSeriesSplit(n_splits)
                    )


start_time = time.time()
regr.fit(X=X_train, y=y_train)
end_time = time.time()

total_time = end_time - start_time

score = train_and_test(regr.best_estimator_, X_train, y_train, X_test, y_test) # We train and test the model another time to get the final score (with the best parameters)

results['LinearReg_pred'] = score
times['LinearReg_pred'] = total_time

print_results("LINEAR REGRESSION PREDEFINED PARAMETERS", regr, score, total_time)

Results of the best estimator of Pipeline
RMSE train: 3254352.60	RMSE test: 3103586.45
MAE train: 2321647.06	MAE test: 2242422.37

---------------------------------------------------
LINEAR REGRESSION PREDEFINED PARAMETERS best model is:

GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),
             estimator=Pipeline(steps=[('scaler', StandardScaler()),
                                       ('model', LinearRegression())]),
             param_grid={'model__fit_intercept': [True]},
             scoring='neg_mean_absolute_error')

Parameters: {'model__fit_intercept': True}

Performance: RMSE train: 3254352.6036904687 | RMSE test: 3103586.448673973 | MAE train: 2321647.059703229 | MAE test: 2242422.3671082137
Execution time: 0.1005866527557373s


np.random.seed(10)
budget = 15
n_splits = 5

all_scores = []

# ! Pipelines
pipeline_lasso = Pipeline([
    ('scaler', StandardScaler()),
    ('select', SelectKBest(f_regression)),
    ('model', Lasso(fit_intercept=True, tol=.5, random_state=10))
])

pipeline_ridge = Pipeline([
    ('scaler', StandardScaler()),
    ('select', SelectKBest(f_regression)),
    ('model', Ridge(fit_intercept=True, tol=.5, random_state=10))
])

pipeline_elastic = Pipeline([
    ('scaler', StandardScaler()),
    ('select', SelectKBest(f_regression)),
    ('model', ElasticNet(fit_intercept=True, tol=.5, random_state=10))
])

# ! Parameter grids
param_grid_lasso = {'model__alpha': np.logspace(-2, 1, 30),
              'select__k': list(range(1, X_train.shape[1]+1))}

pram_grid_ridge = {'model__alpha': np.logspace(-2, 1, 30),
              'select__k': list(range(1, X_train.shape[1]+1))}

param_grid_elastic = {'model__alpha': np.logspace(2.0, 3.0, num=4, base=2.0),
                'model__l1_ratio': np.linspace(0, 1, 40),
                'select__k': list(range(1, X_train.shape[1]+1))}

# ! If we want to use random values for the parameters -> unconsistency in the results
regr_lasso = RandomizedSearchCV(pipeline_lasso,
                                param_grid_lasso,
                                scoring='neg_mean_absolute_error',
                                cv=TimeSeriesSplit(),
                                n_iter=budget,
                                )

regr_ridge = RandomizedSearchCV(pipeline_ridge,
                                pram_grid_ridge,
                                scoring='neg_mean_absolute_error',
                                cv=TimeSeriesSplit(),
                                n_iter=budget,
                                )

regr_elastic = RandomizedSearchCV(pipeline_elastic,
                                  param_grid_elastic,
                                  scoring='neg_mean_absolute_error',
                                  cv=TimeSeriesSplit(),
                                  n_iter=budget,
                                  )

regr = [regr_lasso, regr_ridge, regr_elastic]


ln_reg_time = []

for i in regr:
    start_time = time.time()
    i.fit(X=X_train,y=y_train)
    print(f"Model: {i.best_score_}")
    print(i.best_params_)
    # Now we reevaluate the model on the test set to obtain more accurate results
    all_scores.append(train_and_test(i.best_estimator_, X_train, y_train, X_test, y_test))
    ln_reg_time.append(time.time() - start_time)
    
print(ln_reg_time)

# Select the best model (based on the MAE)
max_score = min(all_scores, key=lambda x: x[3])  # Best model is the one that minimizes the MAE
best_regr = regr[all_scores.index(max_score)]
total_time = ln_reg_time[all_scores.index(max_score)]
 
results['LinearReg_select'] = max_score
print(results)
times['LinearReg_select'] = total_time
print(times)
# print number of selected features
print(f"Number of selected features: {best_regr.best_estimator_.named_steps['select'].get_support().sum()}")

print_results("LINEAR REGRESSION SELECTED PARAMETERS", best_regr, max_score, total_time)

Model: -2723823.0627315757
{'select__k': 41, 'model__alpha': 0.01}
Results of the best estimator of Pipeline
RMSE train: 3767140.00	RMSE test: 3659527.62
MAE train: 2747472.88	MAE test: 2583859.90

Model: -2409149.2610517377
{'select__k': 75, 'model__alpha': 0.17433288221999882}
Results of the best estimator of Pipeline
RMSE train: 3257805.20	RMSE test: 3095921.14
MAE train: 2320591.77	MAE test: 2234624.56

Model: -2826890.3897810644
{'select__k': 30, 'model__l1_ratio': 0.8974358974358974, 'model__alpha': 4.0}
Results of the best estimator of Pipeline
RMSE train: 3758884.17	RMSE test: 3537148.86
MAE train: 2827209.45	MAE test: 2591816.58

[0.7302348613739014, 0.696763277053833, 0.7148227691650391]
{'KNN_pred': [2995411.3954362, 3669004.351737694, 2105354.3342465754, 2589913.2328767125], 'KNN_select': [3386909.0476090116, 3394685.600663936, 2410845.287671233, 2420543.4246575343], 'RegTrees_pred': [0.0, 4413372.497795917, 0.0, 3136761.3698630137], 'RegTrees_select': [3483153.6109944335, 3691730.92222464, 2250097.479452055, 2489240.5479452056], 'LinearReg_pred': [3254352.6036904687, 3103586.448673973, 2321647.059703229, 2242422.3671082137], 'LinearReg_select': [3257805.1961352024, 3095921.136409502, 2320591.76837326, 2234624.5584775107]}
{'KNN_pred': 0.18954920768737793, 'KNN_select': 2.0830042362213135, 'RegTrees_pred': 0.6608903408050537, 'RegTrees_select': 7.55217170715332, 'LinearReg_pred': 0.1005866527557373, 'LinearReg_select': 0.696763277053833}
Number of selected features: 75
---------------------------------------------------
LINEAR REGRESSION SELECTED PARAMETERS best model is:

RandomizedSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),
                   estimator=Pipeline(steps=[('scaler', StandardScaler()),
                                             ('select',
                                              SelectKBest(score_func=<function f_regression at 0x7feb48c353f0>)),
                                             ('model',
                                              Ridge(random_state=10,
                                                    tol=0.5))]),
                   n_iter=15,
                   param_distributions={'model__alpha': array([ 0.01      ,  0.01268961,  0.01610262,  0.020433...
        0.03290345,  0.04175319,  0.05298317,  0.06723358,  0.08531679,
        0.10826367,  0.13738238,  0.17433288,  0.22122163,  0.28072162,
        0.35622479,  0.45203537,  0.57361525,  0.72789538,  0.92367086,
        1.1721023 ,  1.48735211,  1.88739182,  2.39502662,  3.03919538,
        3.85662042,  4.89390092,  6.21016942,  7.88046282, 10.        ]),
                                        'select__k': [1, 2, 3, 4, 5, 6, 7, 8, 9,
                                                      10, 11, 12, 13, 14, 15,
                                                      16, 17, 18, 19, 20, 21,
                                                      22, 23, 24, 25, 26, 27,
                                                      28, 29, 30, ...]},
                   scoring='neg_mean_absolute_error')

Parameters: {'select__k': 75, 'model__alpha': 0.17433288221999882}

Performance: RMSE train: 3257805.1961352024 | RMSE test: 3095921.136409502 | MAE train: 2320591.76837326 | MAE test: 2234624.5584775107
Execution time: 0.696763277053833s


np.random.seed(10)

# ! Obtain best, worst, fastest and slowest model
max_score = max(results.values(), key=lambda x: x[3])  # We determine the best model based on the MAE obtained in the test set (thus, excluding overfitting)
min_score = min(results.values(), key=lambda x: x[3])
# Obtain the key name of the best and worst model
max_time = max(times.values(), key=lambda x: x)
min_time = min(times.values(), key=lambda x: x)

best_model = list(results.keys())[list(results.values()).index(min_score)]
worst_model = list(results.keys())[list(results.values()).index(max_score)]
fastest_model = list(times.keys())[list(times.values()).index(min_time)]
slowest_model = list(times.keys())[list(times.values()).index(max_time)]

print(f"Best model: {best_model} with score {min_score} and time {list(times.values())[list(results.values()).index(min_score)]}s")
print(f"Worst model: {worst_model} with score {max_score} and time {list(times.values())[list(results.values()).index(max_score)]}s")
print(f"Fastest model: {fastest_model} with score {list(results.values())[list(times.values()).index(min_time)]} and time {min_time}s")
print(f"Slowest model: {slowest_model} with score {list(results.values())[list(times.values()).index(max_time)]} and time {max_time}s")


# ! Average (test MAE) score of the models
avg_score = 0
avg_time = 0

for key, value in results.items():
    avg_score += results[key][3]
    avg_time += times[key]

print(f"\nAverage models score: {abs(avg_score/len(results))}")
print(f"Average models time: {avg_time/len(times)}\n")


# ! Differences
print("The score difference between the best and worst model is: ", abs(max_score[3] - min_score)[3])  # test MAE
print("The score difference between the best and fastest model is: ", abs(min_score[3] - list(results.values())[list(times.values()).index(min_time)])[3])  # test MAE
print("The time difference between the best and fastest model model is: ", abs(list(times.values())[list(results.values()).index(min_score)] - min_time))
print("The time difference between the fastest and slowest model is: ", abs(max_time - min_time))

Best model: LinearReg_select with score [3257805.1961352024, 3095921.136409502, 2320591.76837326, 2234624.5584775107] and time 0.696763277053833s
Worst model: RegTrees_pred with score [0.0, 4413372.497795917, 0.0, 3136761.3698630137] and time 0.6608903408050537s
Fastest model: LinearReg_pred with score [3254352.6036904687, 3103586.448673973, 2321647.059703229, 2242422.3671082137] and time 0.1005866527557373s
Slowest model: RegTrees_select with score [3483153.6109944335, 3691730.92222464, 2250097.479452055, 2489240.5479452056] and time 7.55217170715332s

Average models score: 2518917.5834880318
Average models time: 1.880494236946106

The score difference between the best and worst model is:  902136.811385503
The score difference between the best and fastest model is:  7797.808630703017
The time difference between the best and fastest model model is:  0.5961766242980957
The time difference between the fastest and slowest model is:  7.451585054397583


# ! Plot the score
for key, value in results.items():
    plt.bar(key, abs(value[3]))
plt.title('Score')
plt.xlabel('Model')
plt.ylabel('MAE')
plt.tight_layout()

# Exporting image as png to ../data/img folder 
plt.savefig("../data/img/basic_methods_score.png")
plt.show()

# ! Plot the time
for key, value in times.items():
    plt.bar(key, value)
plt.title('Time')
plt.xlabel('Model')
plt.ylabel('Time (s)')
plt.tight_layout()

# Exporting image as png to ../data/img folder - easier to visualize the annotations, better resolution
plt.savefig("../data/img/basic_methods_time.png")
plt.show()


import os

# Export the notebook to HTML
os.system('jupyter nbconvert --to html model.ipynb --output ../data/html/model.html')
print("Notebook exported to HTML")

[NbConvertApp] Converting notebook model.ipynb to html

Notebook exported to HTML

[NbConvertApp] Writing 5305788 bytes to ../data/html/model.html

	apcp_sf1_1	apcp_sf2_1	apcp_sf3_1	apcp_sf4_1	apcp_sf5_1	dlwrf_s1_1	dlwrf_s2_1	dlwrf_s3_1	dlwrf_s4_1	dlwrf_s5_1	dswrf_s1_1	dswrf_s2_1	dswrf_s3_1	dswrf_s4_1	dswrf_s5_1	pres_ms1_1	pres_ms2_1	pres_ms3_1	pres_ms4_1	pres_ms5_1	pwat_ea1_1	pwat_ea2_1	pwat_ea3_1	pwat_ea4_1	pwat_ea5_1	spfh_2m1_1	spfh_2m2_1	spfh_2m3_1	spfh_2m4_1	spfh_2m5_1	tcdc_ea1_1	tcdc_ea2_1	tcdc_ea3_1	tcdc_ea4_1	tcdc_ea5_1	tcolc_e1_1	tcolc_e2_1	tcolc_e3_1	tcolc_e4_1	tcolc_e5_1	tmax_2m1_1	tmax_2m2_1	tmax_2m3_1	tmax_2m4_1	tmax_2m5_1	tmin_2m1_1	tmin_2m2_1	tmin_2m3_1	tmin_2m4_1	tmin_2m5_1	tmp_2m_1_1	tmp_2m_2_1	tmp_2m_3_1	tmp_2m_4_1	tmp_2m_5_1	tmp_sfc1_1	tmp_sfc2_1	tmp_sfc3_1	tmp_sfc4_1	tmp_sfc5_1	ulwrf_s1_1	ulwrf_s2_1	ulwrf_s3_1	ulwrf_s4_1	ulwrf_s5_1	ulwrf_t1_1	ulwrf_t2_1	ulwrf_t3_1	ulwrf_t4_1	ulwrf_t5_1	uswrf_s1_1	uswrf_s2_1	uswrf_s3_1	uswrf_s4_1	uswrf_s5_1	salida
count	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3.650000e+03
mean	0.610222	0.251049	0.479367	0.279969	0.525625	316.590458	316.996492	324.225574	343.169304	342.582550	0.074371	163.928966	376.718929	686.534869	508.429988	101718.580471	101774.517076	101743.013770	101538.253073	101499.397514	21.394485	21.536129	22.127195	22.595594	22.384870	0.007844	0.008848	0.009356	0.009473	0.009918	0.069240	0.067845	0.064862	0.065706	0.062366	0.069539	0.068172	0.065166	0.066036	0.062748	286.950030	288.292227	292.803749	294.483694	294.542492	284.595935	284.638684	284.617400	292.733513	291.084714	284.846286	288.227387	292.740802	294.299550	291.301035	284.094056	289.230769	295.533258	295.904819	290.366407	375.991521	381.989673	400.742449	439.104661	431.318749	247.736467	247.626828	251.950057	262.207928	261.074238	0.078107	38.716712	76.394795	127.098207	99.476613	1.638200e+07
std	2.245850	0.994112	1.756408	1.120933	1.931408	56.119896	58.129352	58.941747	61.150202	61.027007	0.305126	112.645372	159.486316	227.642854	193.753483	725.206610	731.500969	720.701217	699.477989	715.361146	12.256253	12.358856	12.583364	12.633154	12.401121	0.004398	0.005039	0.005175	0.005097	0.005456	0.167104	0.169653	0.171287	0.172516	0.166113	0.166989	0.169522	0.171172	0.172385	0.165958	8.925065	9.743169	9.898253	9.789117	9.776615	8.735982	8.862301	8.866503	9.950300	10.099684	8.722593	9.795209	9.944761	9.795537	10.083859	8.861650	9.756852	9.148308	9.317363	10.462108	46.586515	49.914820	50.766618	53.159310	54.417631	36.270918	36.289003	35.798277	38.698726	38.427066	0.258752	26.010130	30.743175	40.765618	35.505727	8.059674e+06
min	0.000000	0.000000	0.000000	0.000000	0.000000	158.971770	160.032903	165.524543	183.671312	186.342961	0.000000	0.000000	20.000000	30.000000	20.000000	99316.970881	99315.887074	99327.755682	99040.100852	98830.153409	1.100000	1.314819	1.107352	1.142803	1.201246	0.000462	0.000485	0.000451	0.000478	0.000468	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	254.589220	254.937418	258.549777	260.800365	260.863475	251.941358	249.576132	249.576714	258.698331	258.171345	251.942065	254.844406	258.552646	260.795430	258.170049	250.100794	256.360800	263.634377	264.533564	256.520408	229.296161	223.985486	246.314349	278.576630	271.707606	104.671267	113.559602	118.679132	119.393449	121.951425	0.000000	0.000000	3.181818	4.363636	2.545455	5.100000e+05
25%	0.000000	0.000000	0.000000	0.000000	0.000000	270.043573	267.583016	275.008281	292.786299	291.777096	0.000000	52.727273	240.000000	525.454545	344.477273	101266.472124	101311.399680	101283.033381	101102.175426	101049.033203	10.879000	10.718024	11.122964	11.558385	11.559638	0.003991	0.004229	0.004617	0.004736	0.004754	0.000000	0.000000	0.000000	0.000000	0.000000	0.000618	0.000564	0.000545	0.000673	0.000727	280.127903	280.413352	284.916120	286.996939	287.043590	277.844468	277.666665	277.653641	284.828555	283.228638	278.075898	280.285865	284.764714	286.841753	283.495367	277.025516	281.298508	288.689326	288.982901	281.922856	338.180517	340.208757	358.706337	398.061333	388.214025	230.536257	230.759227	234.398558	246.594118	244.431134	0.000000	14.000000	53.818182	108.818182	74.909091	1.061385e+07
50%	0.000000	0.000000	0.000000	0.000000	0.000000	319.801794	321.400251	328.456741	345.402277	345.107513	0.000000	150.000000	384.318182	730.000000	525.636364	101645.975852	101704.351207	101674.750000	101472.419389	101425.127131	19.191209	19.163636	19.650000	20.290909	20.194252	0.007246	0.008248	0.008909	0.009156	0.009518	0.004545	0.004545	0.003636	0.003636	0.002727	0.005118	0.004855	0.004145	0.004345	0.003673	287.597683	289.039583	293.757317	295.529044	295.598358	285.070623	285.192374	285.106049	293.606805	291.806785	285.381022	288.990954	293.704959	295.310534	292.050580	284.581711	290.050444	296.204518	296.669453	291.126846	376.267101	382.791372	401.524648	440.987373	433.520339	253.350231	253.394166	257.342928	270.790095	269.287814	0.000000	35.500000	79.636364	136.636364	105.454545	1.638195e+07
75%	0.114545	0.051818	0.121591	0.033636	0.090000	367.134144	370.342597	378.683015	399.545104	398.891589	0.000000	264.454545	524.636364	893.636364	693.681818	102131.380504	102188.719283	102148.666726	101940.005327	101919.873402	31.188882	31.471632	32.439831	33.103788	32.459132	0.011612	0.013523	0.014275	0.014169	0.015062	0.055455	0.056364	0.042727	0.043636	0.038182	0.056136	0.056918	0.042900	0.043523	0.038843	294.329548	296.940075	301.377123	302.732298	302.753016	292.290495	292.587547	292.576612	301.351051	299.909759	292.551771	296.945783	301.346064	302.621007	300.068969	292.110791	297.882618	303.161016	303.555412	299.639197	416.508387	427.792698	445.458675	482.899051	476.327880	274.309069	274.750930	278.752231	289.945510	289.588822	0.000000	62.000000	103.068182	155.454545	129.727273	2.329185e+07
max	34.428182	16.846364	28.399091	26.381818	36.875455	426.173970	427.486894	429.693146	455.566337	453.910406	3.000000	381.818182	642.181818	990.000000	791.090909	104688.396307	104856.285511	104693.185369	104244.932528	104249.968040	60.327273	58.876881	59.915362	59.309182	60.529133	0.018809	0.019533	0.020985	0.021932	0.023318	1.920909	2.370000	2.449091	2.146364	1.957273	1.920136	2.369282	2.450482	2.146409	1.956655	304.480122	304.792880	311.277519	312.660564	312.668726	300.350930	299.724509	299.735546	310.815957	308.761763	300.344230	304.773410	311.272270	312.595520	308.827304	299.869093	306.834309	315.964081	313.965757	308.270147	470.753102	469.429213	504.584351	555.704024	542.529280	318.245345	311.991660	315.569164	328.920274	327.253141	1.000000	92.272727	192.636364	450.636364	313.909091	3.122700e+07

	apcp_sf4_1	apcp_sf5_1	dlwrf_s1_1	dlwrf_s2_1	dlwrf_s3_1	dlwrf_s4_1	dlwrf_s5_1	dswrf_s2_1	dswrf_s3_1	dswrf_s4_1	dswrf_s5_1	pres_ms1_1	pres_ms2_1	pres_ms3_1	pres_ms4_1	pres_ms5_1	pwat_ea1_1	pwat_ea2_1	pwat_ea3_1	pwat_ea4_1	pwat_ea5_1	spfh_2m1_1	spfh_2m2_1	spfh_2m3_1	spfh_2m4_1	spfh_2m5_1	tcdc_ea1_1	tcdc_ea2_1	tcdc_ea3_1	tcdc_ea4_1	tcdc_ea5_1	tcolc_e1_1	tcolc_e2_1	tcolc_e3_1	tcolc_e4_1	tcolc_e5_1	tmax_2m1_1	tmax_2m2_1	tmax_2m3_1	tmax_2m4_1	tmax_2m5_1	tmin_2m1_1	tmin_2m2_1	tmin_2m3_1	tmin_2m4_1	tmin_2m5_1	tmp_2m_1_1	tmp_2m_2_1	tmp_2m_3_1	tmp_2m_4_1	tmp_2m_5_1	tmp_sfc1_1	tmp_sfc2_1	tmp_sfc3_1	tmp_sfc4_1	tmp_sfc5_1	ulwrf_s1_1	ulwrf_s2_1	ulwrf_s3_1	ulwrf_s4_1	ulwrf_s5_1	ulwrf_t1_1	ulwrf_t2_1	ulwrf_t3_1	ulwrf_t4_1	ulwrf_t5_1	uswrf_s2_1	uswrf_s3_1	uswrf_s4_1	uswrf_s5_1	salida
V1	0.000000	0.0	268.583582	244.241641	251.174486	269.741308	268.377441	30.0	220.000000	510.000000	330.000000	101832.056108	102053.159091	102090.046165	101934.175426	101988.003551	5.879193	7.018182	8.460800	9.418182	9.727869	0.003229	0.002993	0.003775	0.003870	0.003855	0.000000	0.000000	0.000000	0.000000	0.000909	0.000818	0.000264	0.000255	0.000500	0.002218	280.789784	279.627444	285.727761	286.881681	286.885823	279.198020	278.472615	278.474720	285.799685	280.966961	279.249256	279.612202	285.742784	286.841053	280.960865	277.278370	279.250383	288.826760	288.596086	278.500078	341.122231	335.067918	354.626126	397.774053	383.281225	222.153166	252.504475	254.760271	263.342404	260.067843	10.000000	50.000000	106.636364	72.000000	11930700
V2	0.008182	0.2	251.725869	255.824126	272.163913	318.259924	307.929083	30.0	173.636364	333.636364	224.545455	101425.883523	101284.509233	101253.654830	100999.313920	101424.626420	12.534339	11.987316	12.159355	12.313590	13.469729	0.003737	0.003931	0.004015	0.003994	0.004826	0.037273	0.021818	0.101818	0.084545	0.109091	0.037155	0.021309	0.102373	0.085827	0.109336	278.822329	278.063379	283.618583	286.606684	286.643397	277.258919	276.740628	276.740628	283.687009	282.111078	277.282621	278.070390	283.604600	286.554729	282.105011	275.830009	278.269459	287.048970	287.325478	281.005252	330.159915	329.354673	347.524819	388.017767	378.773804	236.836691	233.458263	233.027276	212.652054	222.052916	8.181818	35.909091	58.181818	42.090909	9778500
V3	0.000000	0.0	219.734547	211.996022	216.405820	235.529123	239.840132	30.0	220.000000	523.636364	337.545455	102253.654119	102301.918324	102088.093750	101652.815341	101543.146307	5.726770	5.458528	5.700000	7.163636	9.536364	0.002003	0.001919	0.002107	0.002431	0.002583	0.000000	0.000000	0.007273	0.007273	0.042727	0.001427	0.001582	0.007309	0.006973	0.042127	275.400091	270.222512	275.885787	279.049513	279.381653	269.756037	269.157731	269.156439	276.041792	275.301960	269.766876	270.204285	275.880818	279.064603	275.806757	269.533059	271.690993	281.759993	282.686446	273.615503	309.639845	299.751961	317.250763	364.339136	351.496665	238.655654	232.828737	235.480750	245.177331	238.893102	10.272727	55.272727	118.454545	79.181818	9771900
V4	0.000000	0.0	253.499410	230.896544	235.857221	240.274556	237.804048	30.0	208.181818	512.727273	337.181818	102110.375710	102435.603693	102688.528409	102588.876420	102598.252841	7.889904	6.768959	6.208357	5.977267	6.411838	0.002918	0.002735	0.002771	0.002821	0.002738	0.000000	0.002727	0.005455	0.000909	0.012727	0.000473	0.004018	0.007300	0.001600	0.014882	279.396046	276.176919	276.868630	278.550368	278.572038	276.175482	273.839142	273.840535	276.942990	273.802970	276.312428	274.045715	276.877749	278.571555	273.812827	274.824765	274.466433	281.291418	281.871679	272.191753	330.310971	318.761563	329.305478	360.297788	348.618319	236.784869	241.916776	243.398572	251.473036	247.503769	8.909091	46.000000	107.090909	73.636364	6466800
V5	0.000000	0.0	234.890020	238.927051	246.850822	271.577246	275.572826	30.0	220.000000	517.272727	336.363636	101750.317472	101331.333807	100921.029119	100422.514915	100309.059659	10.783448	10.425542	10.362327	8.829511	9.647615	0.003274	0.003269	0.003066	0.003483	0.003788	0.000909	0.000909	0.000909	0.014545	0.050909	0.001673	0.001836	0.001373	0.015909	0.049591	273.294803	275.018022	283.542744	288.171156	288.265137	272.858415	273.303902	273.306355	283.734819	283.735446	273.314844	274.990234	283.563099	288.178922	285.567946	272.260426	275.132668	285.698725	288.490562	283.121391	310.023179	314.763264	334.042186	388.737835	383.409776	233.641681	233.706659	239.952805	258.128188	253.200684	8.909091	48.909091	106.272727	71.818182	11545200

	apcp_sf1_1	apcp_sf2_1	apcp_sf3_1	apcp_sf4_1	apcp_sf5_1	dlwrf_s1_1	dlwrf_s2_1	dlwrf_s3_1	dlwrf_s4_1	dlwrf_s5_1	dswrf_s1_1	dswrf_s2_1	dswrf_s3_1	dswrf_s4_1	dswrf_s5_1	pres_ms1_1	pres_ms2_1	pres_ms3_1	pres_ms4_1	pres_ms5_1	pwat_ea1_1	pwat_ea2_1	pwat_ea3_1	pwat_ea4_1	pwat_ea5_1	spfh_2m1_1	spfh_2m2_1	spfh_2m3_1	spfh_2m4_1	spfh_2m5_1	tcdc_ea1_1	tcdc_ea2_1	tcdc_ea3_1	tcdc_ea4_1	tcdc_ea5_1	tcolc_e1_1	tcolc_e2_1	tcolc_e3_1	tcolc_e4_1	tcolc_e5_1	tmax_2m1_1	tmax_2m2_1	tmax_2m3_1	tmax_2m4_1	tmax_2m5_1	tmin_2m1_1	tmin_2m2_1	tmin_2m3_1	tmin_2m4_1	tmin_2m5_1	tmp_2m_1_1	tmp_2m_2_1	tmp_2m_3_1	tmp_2m_4_1	tmp_2m_5_1	tmp_sfc1_1	tmp_sfc2_1	tmp_sfc3_1	tmp_sfc4_1	tmp_sfc5_1	ulwrf_s1_1	ulwrf_s2_1	ulwrf_s3_1	ulwrf_s4_1	ulwrf_s5_1	ulwrf_t1_1	ulwrf_t2_1	ulwrf_t3_1	ulwrf_t4_1	ulwrf_t5_1	uswrf_s1_1	uswrf_s2_1	uswrf_s3_1	uswrf_s4_1	uswrf_s5_1	salida
count	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3650.000000	3.650000e+03
mean	0.610222	0.251049	0.479367	0.279969	0.525625	316.590458	316.996492	324.225574	343.169304	342.582550	0.074371	163.928966	376.718929	686.534869	508.429988	101718.580471	101774.517076	101743.013770	101538.253073	101499.397514	21.394485	21.536129	22.127195	22.595594	22.384870	0.007844	0.008848	0.009356	0.009473	0.009918	0.069240	0.067845	0.064862	0.065706	0.062366	0.069539	0.068172	0.065166	0.066036	0.062748	286.950030	288.292227	292.803749	294.483694	294.542492	284.595935	284.638684	284.617400	292.733513	291.084714	284.846286	288.227387	292.740802	294.299550	291.301035	284.094056	289.230769	295.533258	295.904819	290.366407	375.991521	381.989673	400.742449	439.104661	431.318749	247.736467	247.626828	251.950057	262.207928	261.074238	0.078107	38.716712	76.394795	127.098207	99.476613	1.638200e+07
std	2.245850	0.994112	1.756408	1.120933	1.931408	56.119896	58.129352	58.941747	61.150202	61.027007	0.305126	112.645372	159.486316	227.642854	193.753483	725.206610	731.500969	720.701217	699.477989	715.361146	12.256253	12.358856	12.583364	12.633154	12.401121	0.004398	0.005039	0.005175	0.005097	0.005456	0.167104	0.169653	0.171287	0.172516	0.166113	0.166989	0.169522	0.171172	0.172385	0.165958	8.925065	9.743169	9.898253	9.789117	9.776615	8.735982	8.862301	8.866503	9.950300	10.099684	8.722593	9.795209	9.944761	9.795537	10.083859	8.861650	9.756852	9.148308	9.317363	10.462108	46.586515	49.914820	50.766618	53.159310	54.417631	36.270918	36.289003	35.798277	38.698726	38.427066	0.258752	26.010130	30.743175	40.765618	35.505727	8.059674e+06
min	0.000000	0.000000	0.000000	0.000000	0.000000	158.971770	160.032903	165.524543	183.671312	186.342961	0.000000	0.000000	20.000000	30.000000	20.000000	99316.970881	99315.887074	99327.755682	99040.100852	98830.153409	1.100000	1.314819	1.107352	1.142803	1.201246	0.000462	0.000485	0.000451	0.000478	0.000468	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	254.589220	254.937418	258.549777	260.800365	260.863475	251.941358	249.576132	249.576714	258.698331	258.171345	251.942065	254.844406	258.552646	260.795430	258.170049	250.100794	256.360800	263.634377	264.533564	256.520408	229.296161	223.985486	246.314349	278.576630	271.707606	104.671267	113.559602	118.679132	119.393449	121.951425	0.000000	0.000000	3.181818	4.363636	2.545455	5.100000e+05
25%	0.000000	0.000000	0.000000	0.000000	0.000000	270.043573	267.583016	275.008281	292.786299	291.777096	0.000000	52.727273	240.000000	525.454545	344.477273	101266.472124	101311.399680	101283.033381	101102.175426	101049.033203	10.879000	10.718024	11.122964	11.558385	11.559638	0.003991	0.004229	0.004617	0.004736	0.004754	0.000000	0.000000	0.000000	0.000000	0.000000	0.000618	0.000564	0.000545	0.000673	0.000727	280.127903	280.413352	284.916120	286.996939	287.043590	277.844468	277.666665	277.653641	284.828555	283.228638	278.075898	280.285865	284.764714	286.841753	283.495367	277.025516	281.298508	288.689326	288.982901	281.922856	338.180517	340.208757	358.706337	398.061333	388.214025	230.536257	230.759227	234.398558	246.594118	244.431134	0.000000	14.000000	53.818182	108.818182	74.909091	1.061385e+07
50%	0.000000	0.000000	0.000000	0.000000	0.000000	319.801794	321.400251	328.456741	345.402277	345.107513	0.000000	150.000000	384.318182	730.000000	525.636364	101645.975852	101704.351207	101674.750000	101472.419389	101425.127131	19.191209	19.163636	19.650000	20.290909	20.194252	0.007246	0.008248	0.008909	0.009156	0.009518	0.004545	0.004545	0.003636	0.003636	0.002727	0.005118	0.004855	0.004145	0.004345	0.003673	287.597683	289.039583	293.757317	295.529044	295.598358	285.070623	285.192374	285.106049	293.606805	291.806785	285.381022	288.990954	293.704959	295.310534	292.050580	284.581711	290.050444	296.204518	296.669453	291.126846	376.267101	382.791372	401.524648	440.987373	433.520339	253.350231	253.394166	257.342928	270.790095	269.287814	0.000000	35.500000	79.636364	136.636364	105.454545	1.638195e+07
75%	0.114545	0.051818	0.121591	0.033636	0.090000	367.134144	370.342597	378.683015	399.545104	398.891589	0.000000	264.454545	524.636364	893.636364	693.681818	102131.380504	102188.719283	102148.666726	101940.005327	101919.873402	31.188882	31.471632	32.439831	33.103788	32.459132	0.011612	0.013523	0.014275	0.014169	0.015062	0.055455	0.056364	0.042727	0.043636	0.038182	0.056136	0.056918	0.042900	0.043523	0.038843	294.329548	296.940075	301.377123	302.732298	302.753016	292.290495	292.587547	292.576612	301.351051	299.909759	292.551771	296.945783	301.346064	302.621007	300.068969	292.110791	297.882618	303.161016	303.555412	299.639197	416.508387	427.792698	445.458675	482.899051	476.327880	274.309069	274.750930	278.752231	289.945510	289.588822	0.000000	62.000000	103.068182	155.454545	129.727273	2.329185e+07
max	34.428182	16.846364	28.399091	26.381818	36.875455	426.173970	427.486894	429.693146	455.566337	453.910406	3.000000	381.818182	642.181818	990.000000	791.090909	104688.396307	104856.285511	104693.185369	104244.932528	104249.968040	60.327273	58.876881	59.915362	59.309182	60.529133	0.018809	0.019533	0.020985	0.021932	0.023318	1.920909	2.370000	2.449091	2.146364	1.957273	1.920136	2.369282	2.450482	2.146409	1.956655	304.480122	304.792880	311.277519	312.660564	312.668726	300.350930	299.724509	299.735546	310.815957	308.761763	300.344230	304.773410	311.272270	312.595520	308.827304	299.869093	306.834309	315.964081	313.965757	308.270147	470.753102	469.429213	504.584351	555.704024	542.529280	318.245345	311.991660	315.569164	328.920274	327.253141	1.000000	92.272727	192.636364	450.636364	313.909091	3.122700e+07

Práctica 1. Aprendizaje Automático¶

0. Table of contents¶

1. Requirements¶

1.1. Cleaning ../data/img/ folder¶

2. Reading the datasets¶

3. EDA¶

3.0. Dataset preparation¶

3.1. Dataset description¶

3.2. Missing values¶

Observations¶

Conclusions¶

3.3. Outliers¶

3.4. Correlation¶

Getting the correlation matrix formatted into our own data structure¶

Correlation Heat Map¶

4. Train-Test division¶

4.1. Train-Test split¶

4.2. Train-Test RMSE and MAE function¶

4.3. Print model results¶

5. Basic methods:¶

5.1. KNN¶

5.1.1. KNN - Predefined parameters¶

5.1.2. KNN - Selected parameters¶

5.2 Regression Trees¶

5.2.1. Regression Trees - Predefined parameters¶

5.2.2. Regression Trees - Selected parameters¶

5.3 Linnear regression¶

5.3.1. Linear regression - Predefined parameters¶

5.3.2. Linear regression - Selected parameters¶

5.4. Results¶

5.5. Conclusions:¶

6. Reducing Dimensionality¶

X. Output the Jupyter Notebook as an HTML file¶